From e1f76237e019859f5560ec93b8ef2b1f158f4ff1 Mon Sep 17 00:00:00 2001
From: Jan Trmal <yendovy.konference@seznam.cz>
Date: Wed, 22 Jul 2015 12:55:15 -0400
Subject: [PATCH 1/2] windows fixes -- resolving issues with incrrectly
 behaving expf/exp(float) under MSVC2013, plus some other improvements

---
 src/base/kaldi-error.h                        |   2 +-
 src/base/kaldi-math-test.cc                   |  21 ++--
 src/base/kaldi-math.h                         | 102 +++++++++---------
 src/bin/analyze-counts.cc                     |   2 +-
 src/bin/compute-mce-scale.cc                  |   2 +-
 src/bin/get-silence-probs.cc                  |   4 +-
 src/bin/logprob-to-post.cc                    |   2 +-
 src/bin/thresh-post.cc                        |   2 +-
 src/cudamatrix/cu-matrix-test.cc              |   2 +-
 src/cudamatrix/cu-matrix.cc                   |   4 +-
 src/cudamatrix/cu-vector-test.cc              |   4 +-
 src/feat/feature-fbank.cc                     |   4 +-
 src/feat/feature-functions.cc                 |   4 +-
 src/feat/feature-mfcc.cc                      |   4 +-
 src/feat/feature-plp.cc                       |   4 +-
 src/feat/feature-sdc-test.cc                  |   1 +
 src/feat/feature-spectrogram.cc               |   4 +-
 src/feat/online-feature-test.cc               |   4 +-
 src/feat/pitch-functions.cc                   |  34 +++---
 src/feat/resample-test.cc                     |   6 +-
 src/featbin/interpolate-pitch.cc              |   4 +-
 src/featbin/process-pitch-feats.cc            |   4 +-
 src/fstext/determinize-lattice-inl.h          |   6 +-
 src/fstext/fstext-utils-test.cc               |   3 +
 src/fstext/push-special.cc                    |  13 +--
 src/fstext/rescale-inl.h                      |   8 +-
 src/fstext/rescale-test.cc                    |   7 +-
 src/gmm/diag-gmm-test.cc                      |  14 +--
 src/gmm/diag-gmm.cc                           |  10 +-
 src/gmm/ebw-diag-gmm-test.cc                  |   4 +-
 src/gmm/ebw-diag-gmm.cc                       |   4 +-
 src/gmm/full-gmm-test.cc                      |   4 +-
 src/gmm/full-gmm.cc                           |   6 +-
 src/gmm/indirect-diff-diag-gmm.cc             |   2 +-
 src/gmm/mle-am-diag-gmm-test.cc               |   2 +-
 src/gmm/mle-diag-gmm-test.cc                  |   4 +-
 src/gmm/mle-full-gmm-test.cc                  |   2 +-
 src/gmm/model-test-common.cc                  |   4 +-
 src/hmm/hmm-utils.cc                          |   4 +-
 src/hmm/transition-model.cc                   |  32 +++---
 src/ivector/logistic-regression-test.cc       |   2 +-
 src/ivector/logistic-regression.cc            |   2 +-
 src/kws/kws-scoring.cc                        |   3 +-
 src/lat/lattice-functions.cc                  |  10 +-
 src/lat/sausages.cc                           |   6 +-
 src/latbin/lattice-combine.cc                 |   2 +-
 src/latbin/lattice-confidence.cc              |   6 +-
 src/lm/const-arpa-lm.cc                       |   5 +-
 src/matrix/matrix-lib-test.cc                 |  20 ++--
 src/nnet/nnet-component-test.cc               |   1 -
 src/nnet/nnet-rbm.h                           |   2 +-
 src/nnet2/mixup-nnet.cc                       |   2 +-
 src/nnet2/nnet-component.cc                   |   2 +-
 src/nnet2/nnet-compute-discriminative.cc      |   2 +-
 src/nnet2/nnet-compute.cc                     |   2 +-
 src/nnet2/nnet-precondition-online-test.cc    |   2 +-
 src/nnet2/nnet-precondition-online.cc         |   2 +-
 src/nnet2bin/nnet-adjust-priors.cc            |   2 +-
 src/nnet2bin/nnet-modify-learning-rates.cc    |   4 +-
 src/nnetbin/cuda-gpu-available.cc             |   1 +
 src/online/online-audio-source.cc             |   2 +-
 src/online2/online-gmm-decodable.cc           |   2 +-
 src/sgmm/am-sgmm.cc                           |  16 +--
 src/sgmm/estimate-am-sgmm-multi-test.cc       |   5 +-
 src/sgmm/estimate-am-sgmm-test.cc             |   5 +-
 src/sgmm/estimate-am-sgmm.cc                  |   6 +-
 src/sgmm/fmllr-sgmm-test.cc                   |   5 +-
 src/sgmm/sgmm-clusterable.cc                  |   2 +-
 src/sgmm2/am-sgmm2.cc                         |  12 +--
 src/sgmm2/estimate-am-sgmm2-test.cc           |   5 +-
 src/sgmm2/estimate-am-sgmm2.cc                |   4 +-
 src/sgmm2/fmllr-sgmm2-test.cc                 |   5 +-
 .../decodable-am-diag-gmm-regtree.cc          |   4 +-
 src/transform/fmllr-diag-gmm-test.cc          |   4 +-
 src/transform/fmllr-diag-gmm.cc               |   4 +-
 src/transform/fmllr-raw-test.cc               |   4 +-
 src/transform/mllt.cc                         |   4 +-
 src/transform/regression-tree-test.cc         |   2 +-
 src/transform/regtree-fmllr-diag-gmm-test.cc  |   2 +-
 src/util/text-utils.cc                        |  66 +++++++++++-
 windows/generate_solution.pl                  |  30 ++++--
 81 files changed, 365 insertions(+), 251 deletions(-)

diff --git a/src/base/kaldi-error.h b/src/base/kaldi-error.h
index 644849f9963..2285edc0f1c 100644
--- a/src/base/kaldi-error.h
+++ b/src/base/kaldi-error.h
@@ -27,7 +27,7 @@
 #include <sstream>
 #include <cstdio>
 
-#if _MSC_VER >= 0x1400 || !defined(MSC_VER) && __cplusplus > 199711L || defined(__GXX_EXPERIMENTAL_CXX0X__)
+#if _MSC_VER >= 1900 || (!defined(_MSC_VER) && __cplusplus > 199711L) || defined(__GXX_EXPERIMENTAL_CXX0X__)
 #define NOEXCEPT(Predicate) noexcept((Predicate))
 #else
 #define NOEXCEPT(Predicate)
diff --git a/src/base/kaldi-math-test.cc b/src/base/kaldi-math-test.cc
index d002a74f48e..3026f05502f 100644
--- a/src/base/kaldi-math-test.cc
+++ b/src/base/kaldi-math-test.cc
@@ -18,6 +18,7 @@
 // limitations under the License.
 #include "base/kaldi-math.h"
 #include "base/timer.h"
+#include <limits>
 
 namespace kaldi {
 
@@ -25,8 +26,10 @@ template<class I> void UnitTestGcdLcmTpl() {
   for (I a = 1; a < 15; a++) {  // a is min gcd.
     I b = (I)(Rand() % 10);
     I c = (I)(Rand() % 10);
-    if (Rand()%2 == 0 && std::numeric_limits<I>::is_signed) b = -b;
-    if (Rand()%2 == 0 && std::numeric_limits<I>::is_signed) c = -c;
+    if (std::numeric_limits<I>::is_signed) {
+      if (Rand() % 2 == 0) b = -b;
+      if (Rand() % 2 == 0) c = -c;
+    }
     if (b == 0 && c == 0) continue;  // gcd not defined for such numbers.
     I g = Gcd(b*a, c*a);
     KALDI_ASSERT(g >= a);
@@ -157,14 +160,14 @@ void UnitTestLogAddSub() {
   using namespace kaldi;
   for (int i = 0; i < 100; i++) {
     double f1 = Rand() % 10000, f2 = Rand() % 20;
-    double add1 = exp(LogAdd(log(f1), log(f2)));
-    double add2 = exp(LogAdd(log(f2), log(f1)));
+    double add1 = Exp(LogAdd(Log(f1), Log(f2)));
+    double add2 = Exp(LogAdd(Log(f2), Log(f1)));
     double add = f1 + f2, thresh = add*0.00001;
     KALDI_ASSERT(std::abs(add-add1) < thresh && std::abs(add-add2) < thresh);
 
 
     try {
-      double f2_check = exp(LogSub(log(add), log(f1))), thresh = (f2*0.01)+0.001;
+      double f2_check = Exp(LogSub(Log(add), Log(f1))), thresh = (f2*0.01)+0.001;
       KALDI_ASSERT(std::abs(f2_check-f2) < thresh);
     } catch(...) {
       KALDI_ASSERT(f2 == 0);  // It will probably crash for f2=0.
@@ -173,8 +176,8 @@ void UnitTestLogAddSub() {
 }
 
 void UnitTestDefines() {  // Yes, we even unit-test the preprocessor statements.
-  KALDI_ASSERT(exp(kLogZeroFloat) == 0.0);
-  KALDI_ASSERT(exp(kLogZeroDouble) == 0.0);
+  KALDI_ASSERT(Exp(kLogZeroFloat) == 0.0);
+  KALDI_ASSERT(Exp(kLogZeroDouble) == 0.0);
   BaseFloat den = 0.0;
   KALDI_ASSERT(KALDI_ISNAN(0.0 / den));
   KALDI_ASSERT(!KALDI_ISINF(0.0 / den));
@@ -194,8 +197,8 @@ void UnitTestDefines() {  // Yes, we even unit-test the preprocessor statements.
                && "If this test fails, you can probably just comment it out-- may mean your CPU exceeds expected floating point precision");
   KALDI_ASSERT(std::abs(sin(M_PI)) < 1.0e-05 && std::abs(cos(M_PI)+1.0) < 1.0e-05);
   KALDI_ASSERT(std::abs(sin(M_2PI)) < 1.0e-05 && std::abs(cos(M_2PI)-1.0) < 1.0e-05);
-  KALDI_ASSERT(std::abs(sin(exp(M_LOG_2PI))) < 1.0e-05);
-  KALDI_ASSERT(std::abs(cos(exp(M_LOG_2PI)) - 1.0) < 1.0e-05);
+  KALDI_ASSERT(std::abs(sin(Exp(M_LOG_2PI))) < 1.0e-05);
+  KALDI_ASSERT(std::abs(cos(Exp(M_LOG_2PI)) - 1.0) < 1.0e-05);
 }
 
 void UnitTestAssertFunc() {  // Testing Assert** *functions
diff --git a/src/base/kaldi-math.h b/src/base/kaldi-math.h
index 28a02458fc6..edbc8010195 100644
--- a/src/base/kaldi-math.h
+++ b/src/base/kaldi-math.h
@@ -75,6 +75,51 @@
 
 namespace kaldi {
 
+#if !defined(_MSC_VER) || (_MSC_VER >= 1900)
+inline double Exp(double x) { return exp(x); }
+#ifndef KALDI_NO_EXPF
+inline float Exp(float x) { return expf(x); }
+#else
+inline float Exp(float x) { return exp(static_cast<double>(x)); }
+#endif // KALDI_NO_EXPF
+#else
+inline double Exp(double x) { return exp(x); }
+#if !defined(__INTEL_COMPILER) && _MSC_VER == 1800 && defined(_M_X64)
+// Microsoft CL v18.0 buggy 64-bit implementation of
+// expf() incorrectly returns -inf for exp(-inf).
+inline float Exp(float x) { return exp(static_cast<double>(x)); }
+#else
+inline float Exp(float x) { return expf(x); }
+#endif // !defined(__INTEL_COMPILER) && _MSC_VER == 1800 && defined(_M_X64)
+#endif // !defined(_MSC_VER) || (_MSC_VER >= 1900)
+
+inline double Log(double x) { return log(x); }
+inline float Log(float x) { return logf(x); }
+
+#if !defined(_MSC_VER) || (_MSC_VER >= 1700)
+inline double Log1p(double x) {  return log1p(x); }
+inline float Log1p(float x) {  return log1pf(x); }
+#else
+inline double Log1p(double x) {
+  const double cutoff = 1.0e-08;
+  if (x < cutoff)
+    return x - 2 * x * x;
+  else
+    return Log(1.0 + x);
+}
+
+inline float Log1p(float x) {
+  const float cutoff = 1.0e-07;
+  if (x < cutoff)
+    return x - 2 * x * x;
+  else
+    return Log(1.0 + x);
+}
+#endif
+
+static const double kMinLogDiffDouble = Log(DBL_EPSILON);  // negative!
+static const float kMinLogDiffFloat = Log(FLT_EPSILON);  // negative!
+
 // -infinity
 const float kLogZeroFloat = -std::numeric_limits<float>::infinity();
 const double kLogZeroDouble = -std::numeric_limits<double>::infinity();
@@ -103,7 +148,7 @@ inline float RandUniform(struct RandomState* state = NULL) {
 }
 
 inline float RandGauss(struct RandomState* state = NULL) {
-  return static_cast<float>(sqrtf (-2 * logf(RandUniform(state)))
+  return static_cast<float>(sqrtf (-2 * Log(RandUniform(state)))
                             * cosf(2*M_PI*RandUniform(state)));
 }
 
@@ -129,8 +174,6 @@ inline Float RandPrune(Float post, BaseFloat prune_thresh, struct RandomState* s
       (RandUniform(state) <= fabs(post)/prune_thresh ? prune_thresh : 0.0);
 }
 
-static const double kMinLogDiffDouble = std::log(DBL_EPSILON);  // negative!
-static const float kMinLogDiffFloat = std::log(FLT_EPSILON);  // negative!
 
 inline double LogAdd(double x, double y) {
   double diff;
@@ -144,13 +187,7 @@ inline double LogAdd(double x, double y) {
 
   if (diff >= kMinLogDiffDouble) {
     double res;
-#if _MSC_VER >= 1800 || __cplusplus >= 201103L
-    res = x + std::log1p(std::exp(diff));
-#elif defined _MSC_VER
-    res = x + log(1.0 + exp(diff));
-#else
-    res = x + log1p(exp(diff));
-#endif
+    res = x + Log1p(Exp(diff));
     return res;
   } else {
     return x;  // return the larger one.
@@ -170,13 +207,7 @@ inline float LogAdd(float x, float y) {
 
   if (diff >= kMinLogDiffFloat) {
     float res;
-#if _MSC_VER >= 1800 || __cplusplus >= 201103L
-    res = x + std::log1p(std::exp(diff));
-#elif defined _MSC_VER
-    res = x + logf(1.0 + expf(diff));
-#else
-    res = x + log1pf(expf(diff));
-#endif
+    res = x + Log1p(Exp(diff));
     return res;
   } else {
     return x;  // return the larger one.
@@ -194,7 +225,7 @@ inline double LogSub(double x, double y) {
   }
 
   double diff = y - x;  // Will be negative.
-  double res = x + log(1.0 - exp(diff));
+  double res = x + Log(1.0 - Exp(diff));
 
   // res might be NAN if diff ~0.0, and 1.0-exp(diff) == 0 to machine precision
   if (KALDI_ISNAN(res))
@@ -213,7 +244,7 @@ inline float LogSub(float x, float y) {
   }
 
   float diff = y - x;  // Will be negative.
-  float res = x + logf(1.0 - expf(diff));
+  float res = x + Log(1.0f - Exp(diff));
 
   // res might be NAN if diff ~0.0, and 1.0-exp(diff) == 0 to machine precision
   if (KALDI_ISNAN(res))
@@ -301,42 +332,9 @@ template<class I> void Factorize(I m, std::vector<I> *factors) {
 }
 
 inline double Hypot(double x, double y) {  return hypot(x, y); }
-
 inline float Hypot(float x, float y) {  return hypotf(x, y); }
 
-#if !defined(_MSC_VER) || (_MSC_VER >= 1800)
-inline double Log1p(double x) {  return log1p(x); }
-
-inline float Log1p(float x) {  return log1pf(x); }
-#else
-inline double Log1p(double x) {
-    const double cutoff = 1.0e-08;
-    if (x < cutoff)
-        return x - 2 * x * x;
-    else 
-        return log(1.0 + x);
-}
-
-inline float Log1p(float x) {
-    const float cutoff = 1.0e-07;
-    if (x < cutoff)
-        return x - 2 * x * x;
-    else 
-        return log(1.0 + x);
-}
-#endif
-
-inline double Exp(double x) { return exp(x); }
-
-#ifndef KALDI_NO_EXPF
-inline float Exp(float x) { return expf(x); }
-#else
-inline float Exp(float x) { return exp(x); }
-#endif
-
-inline double Log(double x) { return log(x); }
 
-inline float Log(float x) { return logf(x); }
 
 
 }  // namespace kaldi
diff --git a/src/bin/analyze-counts.cc b/src/bin/analyze-counts.cc
index 6fdc06de820..60be710c79d 100644
--- a/src/bin/analyze-counts.cc
+++ b/src/bin/analyze-counts.cc
@@ -111,7 +111,7 @@ int main(int argc, char *argv[]) {
       // sort the counts
       std::vector<std::pair<int32,int32> > sorted_counts;
       for (int32 i = 0; i < counts.size(); i++) {
-        sorted_counts.push_back(std::make_pair(counts[i], i));
+        sorted_counts.push_back(std::make_pair(static_cast<int32>(counts[i]), i));
       }
       std::sort(sorted_counts.begin(), sorted_counts.end());
       
diff --git a/src/bin/compute-mce-scale.cc b/src/bin/compute-mce-scale.cc
index 2f83463c649..349d368a776 100644
--- a/src/bin/compute-mce-scale.cc
+++ b/src/bin/compute-mce-scale.cc
@@ -71,7 +71,7 @@ int main(int argc, char *argv[]) {
         // means it's more comparable with MMI/MPE.
         BaseFloat den_score = den_score_reader.Value(key);
         BaseFloat score_difference = mce_alpha * (num_score - den_score) + mce_beta;
-        BaseFloat sigmoid_difference = 1.0 / (1.0 + exp(score_difference));
+        BaseFloat sigmoid_difference = 1.0 / (1.0 + Exp(score_difference));
         // It might be more natural to make the scale
         //
         BaseFloat scale = 4.0 * sigmoid_difference * (1 - sigmoid_difference);
diff --git a/src/bin/get-silence-probs.cc b/src/bin/get-silence-probs.cc
index 6aa06301f14..b440399654e 100644
--- a/src/bin/get-silence-probs.cc
+++ b/src/bin/get-silence-probs.cc
@@ -66,7 +66,7 @@ int main(int argc, char *argv[]) {
     KALDI_ASSERT(quantize >= 0.0 && quantize <= 1.0);
 
 
-    double sil_log_bias = log(sil_prior / (1.0 - sil_prior));
+    double sil_log_bias = Log(sil_prior / (1.0 - sil_prior));
     
     std::string silence_likes_rspecifier = po.GetArg(1),
         nonsilence_likes_rspecifier = po.GetArg(2),
@@ -104,7 +104,7 @@ int main(int argc, char *argv[]) {
         if (sil_loglike > 10) {
           sil_probs(f) = 1.0; // because the exp below might fail.
         } else {
-          BaseFloat e_sil_loglike = exp(sil_loglike);
+          BaseFloat e_sil_loglike = Exp(sil_loglike);
           BaseFloat sil_prob = e_sil_loglike / (1.0 + e_sil_loglike);
           if ( !(sil_prob >= 0.0 && sil_prob  <= 1.0)) {
             KALDI_WARN << "Bad silence prob (NaNs found?), setting to 0.5";
diff --git a/src/bin/logprob-to-post.cc b/src/bin/logprob-to-post.cc
index c9f21ee6459..f221580a484 100644
--- a/src/bin/logprob-to-post.cc
+++ b/src/bin/logprob-to-post.cc
@@ -80,7 +80,7 @@ int main(int argc, char *argv[]) {
       for (int32 i = 0; i < logprobs.NumRows(); i++) {
         SubVector<BaseFloat> row(logprobs, i);
         for (int32 j = 0; j < row.Dim(); j++) {
-          BaseFloat p = exp(row(j));
+          BaseFloat p = Exp(row(j));
           if (p >= min_post) {
             post[i].push_back(std::make_pair(j, p));
           } else if (random_prune && (p / min_post) >= RandUniform()) {
diff --git a/src/bin/thresh-post.cc b/src/bin/thresh-post.cc
index 67bf05fac40..91287306f23 100644
--- a/src/bin/thresh-post.cc
+++ b/src/bin/thresh-post.cc
@@ -78,7 +78,7 @@ int main(int argc, char *argv[]) {
           if (weight < threshold) weight *= scale;
           total_weight_out += weight;
           if (weight != 0.0)
-            new_post[i].push_back(std::make_pair(tid, weight));
+            new_post[i].push_back(std::make_pair(tid, static_cast<BaseFloat>(weight)));
         }
       }
       posterior_writer.Write(posterior_reader.Key(), new_post);
diff --git a/src/cudamatrix/cu-matrix-test.cc b/src/cudamatrix/cu-matrix-test.cc
index a19c8b9c987..629920a1bb5 100644
--- a/src/cudamatrix/cu-matrix-test.cc
+++ b/src/cudamatrix/cu-matrix-test.cc
@@ -1621,7 +1621,7 @@ static void UnitTestCuDiffXent() {
   //cpu
   for(MatrixIndexT r=0; r<Hi.NumRows(); r++) {
     int32 col_tgt = Htgt[r];
-    Hlogpost(r) = log(Hi(r, col_tgt));
+    Hlogpost(r) = Log(Hi(r, col_tgt));
     Hi(r, col_tgt) -= 1.0;
   }
 
diff --git a/src/cudamatrix/cu-matrix.cc b/src/cudamatrix/cu-matrix.cc
index 91267adea0a..9ba1c599ddd 100644
--- a/src/cudamatrix/cu-matrix.cc
+++ b/src/cudamatrix/cu-matrix.cc
@@ -1193,7 +1193,7 @@ void CuMatrix<Real>::CompObjfAndDeriv(const std::vector<MatrixElement<Real> >& s
       //KALDI_ASSERT(label >= 0 && label < nnet_.OutputDim());
       Real this_prob = output(m, label);
       KALDI_ASSERT(this_prob >= 0.99e-20); // we floored to 1.0e-20 in SoftmaxLayer.
-      *tot_objf += weight * log(this_prob);
+      *tot_objf += weight * Log(this_prob);
       *tot_weight += weight;
       (*this)(m, label) += weight / this_prob; 
     }
@@ -1399,7 +1399,7 @@ void CuMatrixBase<Real>::DiffXent(const CuArray<int32> &tgt,
     for(int32 r = 0; r < num_rows; r++) {
       int32 col_tgt = tgt.Data()[r];
       Real &value = Mat()(r, col_tgt);
-      log_post_tgt->Vec()(r) = log(value);
+      log_post_tgt->Vec()(r) = Log(value);
       value -= 1.0;
     }
   }
diff --git a/src/cudamatrix/cu-vector-test.cc b/src/cudamatrix/cu-vector-test.cc
index 99fff9ee29b..db715d75d7a 100644
--- a/src/cudamatrix/cu-vector-test.cc
+++ b/src/cudamatrix/cu-vector-test.cc
@@ -463,7 +463,7 @@ template<typename Real> void CuVectorUnitTestApplyExp() {
   vector.ApplyExp();
   for(int32 j = 0; j < dim; j++) {
     //std::cout<<"diff is "<<exp(vector2(j))-vector(j)<<std::endl;;
-    KALDI_ASSERT(std::abs(exp(vector2(j))-vector(j)) < 0.000001 );
+    KALDI_ASSERT(std::abs(Exp(vector2(j))-vector(j)) < 0.000001 );
   }
 
 }
@@ -482,7 +482,7 @@ template<typename Real> void CuVectorUnitTestApplyLog() {
   vector.ApplyLog();
   for(int32 j = 0; j < dim; j++) {
     //std::cout<<"diff is "<<exp(vector2(j))-vector(j)<<std::endl;;
-    KALDI_ASSERT(std::abs(log(vector2(j))-vector(j)) < 0.000001 );
+    KALDI_ASSERT(std::abs(Log(vector2(j))-vector(j)) < 0.000001 );
   }
 }
 
diff --git a/src/feat/feature-fbank.cc b/src/feat/feature-fbank.cc
index d28e874549e..bac61ed2059 100644
--- a/src/feat/feature-fbank.cc
+++ b/src/feat/feature-fbank.cc
@@ -26,7 +26,7 @@ namespace kaldi {
 Fbank::Fbank(const FbankOptions &opts)
     : opts_(opts), feature_window_function_(opts.frame_opts), srfft_(NULL) {
   if (opts.energy_floor > 0.0)
-    log_energy_floor_ = log(opts.energy_floor);
+    log_energy_floor_ = Log(opts.energy_floor);
 
   int32 padded_window_size = opts.frame_opts.PaddedWindowSize();
   if ((padded_window_size & (padded_window_size-1)) == 0)  // Is a power of two...
@@ -136,7 +136,7 @@ void Fbank::ComputeInternal(const VectorBase<BaseFloat> &wave,
 
     // Compute energy after window function (not the raw one)
     if (opts_.use_energy && !opts_.raw_energy)
-      log_energy = log(std::max(VecVec(window, window),
+      log_energy = Log(std::max(VecVec(window, window),
                                 std::numeric_limits<BaseFloat>::min()));
 
     if (srfft_ != NULL)  // Compute FFT using split-radix algorithm.
diff --git a/src/feat/feature-functions.cc b/src/feat/feature-functions.cc
index 2de7f49e58a..9678e909a5a 100644
--- a/src/feat/feature-functions.cc
+++ b/src/feat/feature-functions.cc
@@ -153,7 +153,7 @@ void ExtractWindow(const VectorBase<BaseFloat> &wave,
   if (log_energy_pre_window != NULL) {
     BaseFloat energy = std::max(VecVec(window_part, window_part),
                                 std::numeric_limits<BaseFloat>::min());
-    *log_energy_pre_window = log(energy);
+    *log_energy_pre_window = Log(energy);
   }
 
   if (opts.preemph_coeff != 0.0)
@@ -385,7 +385,7 @@ BaseFloat ComputeLpc(const VectorBase<BaseFloat> &autocorr_in,
                           tmp.Data());
   if (ans <= 0.0)
     KALDI_WARN << "Zero energy in LPC computation";
-  return -log((double)1.0/ans);  // forms the C0 value
+  return -Log((double)1.0/ans);  // forms the C0 value
 }
 
 void SpliceFrames(const MatrixBase<BaseFloat> &input_features,
diff --git a/src/feat/feature-mfcc.cc b/src/feat/feature-mfcc.cc
index 2960e9ed1d5..135152733d9 100644
--- a/src/feat/feature-mfcc.cc
+++ b/src/feat/feature-mfcc.cc
@@ -39,7 +39,7 @@ Mfcc::Mfcc(const MfccOptions &opts)
     ComputeLifterCoeffs(opts.cepstral_lifter, &lifter_coeffs_);
   }
   if (opts.energy_floor > 0.0)
-    log_energy_floor_ = log(opts.energy_floor);
+    log_energy_floor_ = Log(opts.energy_floor);
 
   int32 padded_window_size = opts.frame_opts.PaddedWindowSize();
   if ((padded_window_size & (padded_window_size-1)) == 0)  // Is a power of two...
@@ -138,7 +138,7 @@ void Mfcc::ComputeInternal(const VectorBase<BaseFloat> &wave,
                   (opts_.use_energy && opts_.raw_energy ? &log_energy : NULL));
 
     if (opts_.use_energy && !opts_.raw_energy)
-      log_energy = log(std::max(VecVec(window, window),
+      log_energy = Log(std::max(VecVec(window, window),
                                 std::numeric_limits<BaseFloat>::min()));
 
     if (srfft_ != NULL)  // Compute FFT using the split-radix algorithm.
diff --git a/src/feat/feature-plp.cc b/src/feat/feature-plp.cc
index a22dd3ca97b..fe439864346 100644
--- a/src/feat/feature-plp.cc
+++ b/src/feat/feature-plp.cc
@@ -34,7 +34,7 @@ Plp::Plp(const PlpOptions &opts)
                 &idft_bases_);
 
   if (opts.energy_floor > 0.0)
-    log_energy_floor_ = log(opts.energy_floor);
+    log_energy_floor_ = Log(opts.energy_floor);
 
   int32 padded_window_size = opts.frame_opts.PaddedWindowSize();
   if ((padded_window_size & (padded_window_size-1)) == 0)  // Is a power of two...
@@ -193,7 +193,7 @@ void Plp::ComputeInternal(const VectorBase<BaseFloat> &wave,
                   (opts_.use_energy && opts_.raw_energy ? &log_energy : NULL));
 
     if (opts_.use_energy && !opts_.raw_energy)
-      log_energy = log(std::max(VecVec(window, window),
+      log_energy = Log(std::max(VecVec(window, window),
                                 std::numeric_limits<BaseFloat>::min()));
 
     if (srfft_ != NULL)  // Compute FFT using split-radix algorithm.
diff --git a/src/feat/feature-sdc-test.cc b/src/feat/feature-sdc-test.cc
index 1ffaebc5f7e..3ecef148b87 100644
--- a/src/feat/feature-sdc-test.cc
+++ b/src/feat/feature-sdc-test.cc
@@ -162,6 +162,7 @@ int main() {
     }
     return 0;
   } catch (const std::exception &e) {
+    static_cast<void>(e);
     return 1;
   }
   
diff --git a/src/feat/feature-spectrogram.cc b/src/feat/feature-spectrogram.cc
index 76a4849c7d5..df915ad90fe 100644
--- a/src/feat/feature-spectrogram.cc
+++ b/src/feat/feature-spectrogram.cc
@@ -27,7 +27,7 @@ namespace kaldi {
 Spectrogram::Spectrogram(const SpectrogramOptions &opts)
     : opts_(opts), feature_window_function_(opts.frame_opts), srfft_(NULL) {
   if (opts.energy_floor > 0.0)
-    log_energy_floor_ = log(opts.energy_floor);
+    log_energy_floor_ = Log(opts.energy_floor);
 
   int32 padded_window_size = opts.frame_opts.PaddedWindowSize();
   if ((padded_window_size & (padded_window_size-1)) == 0)  // Is a power of two
@@ -68,7 +68,7 @@ void Spectrogram::Compute(const VectorBase<BaseFloat> &wave,
 
     // Compute energy after window function (not the raw one)
     if (!opts_.raw_energy)
-      log_energy = log(std::max(VecVec(window, window),
+      log_energy = Log(std::max(VecVec(window, window),
                                 std::numeric_limits<BaseFloat>::min()));
     
     if (srfft_ != NULL)  // Compute FFT using split-radix algorithm.
diff --git a/src/feat/online-feature-test.cc b/src/feat/online-feature-test.cc
index 55df9914490..7a46e837151 100644
--- a/src/feat/online-feature-test.cc
+++ b/src/feat/online-feature-test.cc
@@ -70,6 +70,7 @@ bool RandomSplit(int32 wav_dim,
                  int32 num_pieces,
                  int32 trials = 5) {
   piece_dim->clear();
+  piece_dim->resize(num_pieces, 0);
 
   int32 dim_mean = wav_dim / (num_pieces * 2);
   int32 cnt = 0;
@@ -182,7 +183,8 @@ void TestOnlineMfcc() {
   // we try to break it into from 5 pieces to 9(not essential to do so)
   for (int32 num_piece = 5; num_piece < 10; num_piece++) {
     OnlineMfcc online_mfcc(op);
-    std::vector<int32> piece_length(num_piece);
+    std::vector<int32> piece_length(num_piece, 0);
+
     bool ret = RandomSplit(waveform.Dim(), &piece_length, num_piece);
     KALDI_ASSERT(ret);
 
diff --git a/src/feat/pitch-functions.cc b/src/feat/pitch-functions.cc
index c61fb5f483c..cb934a09ac2 100644
--- a/src/feat/pitch-functions.cc
+++ b/src/feat/pitch-functions.cc
@@ -77,10 +77,10 @@ BaseFloat NccfToPov(BaseFloat n) {
   BaseFloat ndash = fabs(n);
   if (ndash > 1.0) ndash = 1.0;  // just in case it was slightly outside [-1, 1]
 
-  BaseFloat r = -5.2 + 5.4 * exp(7.5 * (ndash - 1.0)) + 4.8 * ndash -
-                2.0 * exp(-10.0 * ndash) + 4.2 * exp(20.0 * (ndash - 1.0));
+  BaseFloat r = -5.2 + 5.4 * Exp(7.5 * (ndash - 1.0)) + 4.8 * ndash -
+                2.0 * Exp(-10.0 * ndash) + 4.2 * Exp(20.0 * (ndash - 1.0));
   // r is the approximate log-prob-ratio of voicing, log(p/(1-p)).
-  BaseFloat p = 1.0 / (1 + exp(-1.0 * r));
+  BaseFloat p = 1.0 / (1 + Exp(-1.0 * r));
   KALDI_ASSERT(p - p == 0);  // Check for NaN/inf
   return p;
 }
@@ -210,7 +210,7 @@ class PitchFrameInfo {
   /// info for the final state; the iterator will be decremented inside this
   /// function. 
   void SetBestState(int32 best_state,
-      std::vector<std::pair<int32, BaseFloat> >::iterator lag_nccf_iter);
+      std::vector<std::pair<int32, BaseFloat> > &lag_nccf);
 
   /// This function may be called on the last (most recent) PitchFrameInfo
   /// object; it computes how many frames of latency there is because the
@@ -313,7 +313,7 @@ void PitchFrameInfo::ComputeBacktraces(
   Vector<BaseFloat> local_cost(num_states, kUndefined);
   ComputeLocalCost(nccf_pitch, lags, opts, &local_cost);
 
-  const BaseFloat delta_pitch_sq = pow(log(1.0 + opts.delta_pitch), 2.0),
+  const BaseFloat delta_pitch_sq = pow(Log(1.0 + opts.delta_pitch), 2.0),
       inter_frame_factor = delta_pitch_sq * opts.penalty_factor;
 
   // index local_cost, prev_forward_cost and this_forward_cost using raw pointer
@@ -483,10 +483,14 @@ void PitchFrameInfo::ComputeBacktraces(
 
 void PitchFrameInfo::SetBestState(
     int32 best_state,
-    std::vector<std::pair<int32, BaseFloat> >::iterator iter) {
+    std::vector<std::pair<int32, BaseFloat> > &lag_nccf) {
+
   // This function would naturally be recursive, but we have coded this to avoid
   // recursion, which would otherwise eat up the stack.  Think of it as a static
   // member function, except we do use "this" right at the beginning.
+
+  std::vector<std::pair<int32, BaseFloat> >::reverse_iterator iter = lag_nccf.rbegin();
+
   PitchFrameInfo *this_info = this;  // it will change in the loop.
   while (this_info != NULL) {
     PitchFrameInfo *prev_info = this_info->prev_info_;
@@ -501,7 +505,7 @@ void PitchFrameInfo::SetBestState(
     if (prev_info != NULL)  // don't write anything for frame -1.
       iter->second = this_info->state_info_[state_info_index].pov_nccf;
     this_info = prev_info;
-    iter--;
+    if (this_info != NULL) ++iter;
   }
 }
 
@@ -989,9 +993,7 @@ void OnlinePitchFeatureImpl::RecomputeBacktraces() {
   if (lag_nccf_.size() != static_cast<size_t>(num_frames))
     lag_nccf_.resize(num_frames);
   
-  std::vector<std::pair<int32, BaseFloat> >::iterator last_iter =
-      lag_nccf_.end() - 1;
-  frame_info_.back()->SetBestState(best_final_state, last_iter);  
+  frame_info_.back()->SetBestState(best_final_state, lag_nccf_);
   frames_latency_ =
       frame_info_.back()->ComputeLatency(opts_.max_frames_latency);
   for (size_t i = 0; i < nccf_info_.size(); i++)
@@ -1146,9 +1148,7 @@ void OnlinePitchFeatureImpl::AcceptWaveform(
   int32 best_final_state;
   forward_cost_.Min(&best_final_state);
   lag_nccf_.resize(frame_info_.size() - 1);  // will keep any existing data.
-  std::vector<std::pair<int32, BaseFloat> >::iterator last_iter =
-      lag_nccf_.end() - 1;
-  frame_info_.back()->SetBestState(best_final_state, last_iter);
+  frame_info_.back()->SetBestState(best_final_state, lag_nccf_);
   frames_latency_ =
       frame_info_.back()->ComputeLatency(opts_.max_frames_latency);
   KALDI_VLOG(4) << "Latency is " << frames_latency_;
@@ -1425,7 +1425,7 @@ BaseFloat OnlineProcessPitch::GetRawLogPitchFeature(int32 frame) const {
   src_->GetFrame(frame, &tmp);
   BaseFloat pitch = tmp(1);
   KALDI_ASSERT(pitch > 0);
-  return log(pitch);
+  return Log(pitch);
 }
 
 BaseFloat OnlineProcessPitch::GetNormalizedLogPitchFeature(int32 frame) {
@@ -1487,7 +1487,7 @@ void OnlineProcessPitch::UpdateNormalizationStats(int32 frame) {
         Vector<BaseFloat> tmp(kRawFeatureDim);
         src_->GetFrame(prev_window_begin, &tmp);
         BaseFloat accurate_pov = NccfToPov(tmp(0)),
-            log_pitch = log(tmp(1));
+            log_pitch = Log(tmp(1));
         this_stats.sum_pov -= accurate_pov;
         this_stats.sum_log_pitch_pov -= accurate_pov * log_pitch;
       }
@@ -1496,7 +1496,7 @@ void OnlineProcessPitch::UpdateNormalizationStats(int32 frame) {
         Vector<BaseFloat> tmp(kRawFeatureDim);
         src_->GetFrame(prev_window_end, &tmp);
         BaseFloat accurate_pov = NccfToPov(tmp(0)),
-            log_pitch = log(tmp(1));
+            log_pitch = Log(tmp(1));
         this_stats.sum_pov += accurate_pov;
         this_stats.sum_log_pitch_pov += accurate_pov * log_pitch;
       }
@@ -1515,7 +1515,7 @@ void OnlineProcessPitch::UpdateNormalizationStats(int32 frame) {
   for (int32 f = this_window_begin; f < this_window_end; f++) {
     src_->GetFrame(f, &tmp);
     BaseFloat accurate_pov = NccfToPov(tmp(0)),
-        log_pitch = log(tmp(1));    
+        log_pitch = Log(tmp(1));
     this_stats.sum_pov += accurate_pov;
     this_stats.sum_log_pitch_pov += accurate_pov * log_pitch;
   }
diff --git a/src/feat/resample-test.cc b/src/feat/resample-test.cc
index b524e73c07d..68d9f501938 100644
--- a/src/feat/resample-test.cc
+++ b/src/feat/resample-test.cc
@@ -133,7 +133,7 @@ void UnitTestArbitraryResample() {
       if (num_zeros == 3) {
         KALDI_ASSERT(error < 0.1);
       } else {
-        KALDI_ASSERT(error < 0.02);
+        KALDI_ASSERT(error < 0.025);
       }
     } else {
       KALDI_VLOG(1) << "[not checking since out of bounds]";
@@ -225,7 +225,7 @@ void UnitTestLinearResample() {
 }
 
 void UnitTestLinearResample2() {
-  int32 num_samp = 100 + rand() % 100;
+  int32 num_samp = 150 + rand() % 100;
   BaseFloat samp_freq = 1000, resamp_freq = 4000;
 
   int32 num_zeros = 10; // fairly accurate.
@@ -257,7 +257,7 @@ void UnitTestLinearResample2() {
   linear_resampler2.Resample(signal_upsampled, true, &signal_downsampled);
 
 
-  int32 samp_discard = 20;  // Discard 20 samples for edge effects.
+  int32 samp_discard = 30;  // Discard 20 samples for edge effects.
   SubVector<BaseFloat> signal_middle(signal, samp_discard,
                                      signal.Dim() - (2 * samp_discard));
 
diff --git a/src/featbin/interpolate-pitch.cc b/src/featbin/interpolate-pitch.cc
index 747f95a5824..45b67c54fc3 100644
--- a/src/featbin/interpolate-pitch.cc
+++ b/src/featbin/interpolate-pitch.cc
@@ -139,8 +139,8 @@ class PitchInterpolator {
     BaseFloat constant_prob = (1.0 - p_voicing_[t]) * opts_.interpolator_factor,
         specified_prob = p_voicing_[t] + constant_prob;
     // specified_prob adds in the extra probability mass at the observed pitch value.
-    BaseFloat log_constant_prob = log(constant_prob),
-        log_ratio = log(specified_prob / constant_prob);
+    BaseFloat log_constant_prob = Log(constant_prob),
+        log_ratio = Log(specified_prob / constant_prob);
     log_alpha_.Add(log_constant_prob); // add log_constant_prob to all pitches at this time.
     
     log_alpha_(pitch_[t]) += log_ratio; // corrects this to be like adding
diff --git a/src/featbin/process-pitch-feats.cc b/src/featbin/process-pitch-feats.cc
index 76b259532ba..72ab7ee2924 100644
--- a/src/featbin/process-pitch-feats.cc
+++ b/src/featbin/process-pitch-feats.cc
@@ -32,7 +32,7 @@ void ProcessPovFeatures(Matrix<BaseFloat> *mat) {
   for (int32 i = 0; i < num_frames; i++) {
     BaseFloat p = (*mat)(i, 0);
     KALDI_ASSERT(p >= 0.0 && p <= 1.0);
-    (*mat)(i, 0) = log((p + 0.0001) / (1.0001 - p));
+    (*mat)(i, 0) = Log((p + 0.0001) / (1.0001 - p));
   }
 }
 
@@ -40,7 +40,7 @@ void TakeLogOfPitch(Matrix<BaseFloat> *mat) {
   int32 num_frames = mat->NumRows();
   for (int32 i = 0; i < num_frames; i++) {
     KALDI_ASSERT((*mat)(i, 1) > 0.0);
-    (*mat)(i, 1) = log((*mat)(i, 1));
+    (*mat)(i, 1) = Log((*mat)(i, 1));
   }
 }
 
diff --git a/src/fstext/determinize-lattice-inl.h b/src/fstext/determinize-lattice-inl.h
index 9a3f8169dab..b41deb980ee 100644
--- a/src/fstext/determinize-lattice-inl.h
+++ b/src/fstext/determinize-lattice-inl.h
@@ -149,13 +149,15 @@ template<class IntType> class LatticeStringRepository {
   void ConvertToVector(const Entry *entry, vector<IntType> *out) const {
     size_t length = Size(entry);
     out->resize(length);
-    typename vector<IntType>::iterator iter = out->end() - 1;
+    if (entry != NULL) {
+      typename vector<IntType>::reverse_iterator iter = out->rbegin();
     while (entry != NULL) {
       *iter = entry->i;
       entry = entry->parent;
-      --iter;
+        ++iter;
     }
   }
+  }
 
   const Entry *ConvertFromVector(const vector<IntType> &vec) {
     const Entry *e = NULL;
diff --git a/src/fstext/fstext-utils-test.cc b/src/fstext/fstext-utils-test.cc
index b697e20a913..1233491baf6 100644
--- a/src/fstext/fstext-utils-test.cc
+++ b/src/fstext/fstext-utils-test.cc
@@ -103,6 +103,9 @@ template<class Arc>  void TestSafeDeterminizeWrapper() {  // also tests SafeDete
   int n_syms = 2 + kaldi::Rand() % 5, n_states = 3 + kaldi::Rand() % 10, n_arcs = 5 + kaldi::Rand() % 30, n_final = 1 + kaldi::Rand()%3;  // Up to 2 unique symbols.
   cout << "Testing pre-determinize with "<<n_syms<<" symbols, "<<n_states<<" states and "<<n_arcs<<" arcs and "<<n_final<<" final states.\n";
   SymbolTable *sptr = new SymbolTable("my-symbol-table");
+  sptr->AddSymbol("<eps>");
+  delete sptr;
+  sptr = new SymbolTable("my-symbol-table");
 
   vector<Label> all_syms;  // including epsilon.
   // Put symbols in the symbol table from 1..n_syms-1.
diff --git a/src/fstext/push-special.cc b/src/fstext/push-special.cc
index 951f4a607d4..16cc9b41af3 100644
--- a/src/fstext/push-special.cc
+++ b/src/fstext/push-special.cc
@@ -20,6 +20,7 @@
 
 #include "fstext/push-special.h"
 #include "base/kaldi-error.h"
+#include "base/kaldi-math.h"
 
 namespace fst {
 
@@ -101,10 +102,10 @@ class PushSpecialClass {
            !aiter.Done(); aiter.Next()) {
         const Arc &arc = aiter.Value();
         StateId t = arc.nextstate;
-        double weight = exp(-arc.weight.Value());
+        double weight = kaldi::Exp(-arc.weight.Value());
         pred_[t].push_back(std::make_pair(s, weight));
       }
-      double final = exp(-fst_->Final(s).Value());
+      double final = kaldi::Exp(-fst_->Final(s).Value());
       if (final != 0.0)
         pred_[initial_state_].push_back(std::make_pair(s, final));
     }
@@ -121,9 +122,9 @@ class PushSpecialClass {
            !aiter.Done(); aiter.Next()) {
         const Arc &arc = aiter.Value();
         StateId t = arc.nextstate;
-        sum += exp(-arc.weight.Value()) * occ_[t] / occ_[s];
+        sum += kaldi::Exp(-arc.weight.Value()) * occ_[t] / occ_[s];
       }
-      sum += exp(-(fst_->Final(s).Value())) * occ_[initial_state_] / occ_[s];
+      sum += kaldi::Exp(-(fst_->Final(s).Value())) * occ_[initial_state_] / occ_[s];
       if (s == 0) {
         min_sum = sum;
         max_sum = sum;
@@ -133,7 +134,7 @@ class PushSpecialClass {
       }
     }
     KALDI_VLOG(4) << "min,max is " << min_sum << " " << max_sum;
-    return log(max_sum / min_sum); // In FST world we'll actually
+    return kaldi::Log(max_sum / min_sum); // In FST world we'll actually
     // dealing with logs, so the log of the ratio is more suitable
     // to compare with delta (makes testing the algorithm easier).
   }
@@ -187,7 +188,7 @@ class PushSpecialClass {
     // First get the potentials as negative-logs, like the values
     // in the FST.
     for (StateId s = 0; s < num_states_; s++) {
-      occ_[s] = -log(occ_[s]);
+      occ_[s] = -kaldi::Log(occ_[s]);
       if (KALDI_ISNAN(occ_[s]) || KALDI_ISINF(occ_[s]))
         KALDI_WARN << "NaN or inf found: " << occ_[s];
     }
diff --git a/src/fstext/rescale-inl.h b/src/fstext/rescale-inl.h
index 86f38170638..e7e237fef61 100644
--- a/src/fstext/rescale-inl.h
+++ b/src/fstext/rescale-inl.h
@@ -21,6 +21,7 @@
 #define KALDI_FSTEXT_RESCALE_INL_H_
 #include <cstring>
 #include "base/kaldi-common.h"
+#include "base/kaldi-math.h"
 #include "util/stl-utils.h"
 #include "fstext/fstext-utils.h"
 
@@ -119,7 +120,7 @@ inline LogWeight RescaleToStochastic(MutableFst<LogArc> *fst,
     return Weight::One();  // can't rescale empty FST.
 
   // total weight).
-  Weight max = Weight(-log(2.0));
+  Weight max = Weight(-kaldi::Log(2.0));
 
   // upper_bound and lower_bound are in terms of weight.Value(),
   // in terms of weight they would have the reversed names.
@@ -132,6 +133,11 @@ inline LogWeight RescaleToStochastic(MutableFst<LogArc> *fst,
   Weight cur_rescale = Weight::One();
   Weight cur_tot;
   while (1) {
+    {
+      FstPrinter<LogArc> fstprinter(*fst, NULL, NULL, NULL, false, true);
+      fstprinter.Print(&std::cout, "standard output");
+    }
+
     cur_tot = ComputeTotalWeight(*fst, max, delta);
     std::cerr << "Current rescaling factor is " << cur_rescale <<", total is: " << cur_tot << '\n';
     if (cur_tot.Value() < Weight::One().Value()) {  // read as: cur_tot > 1.
diff --git a/src/fstext/rescale-test.cc b/src/fstext/rescale-test.cc
index 70d3439b07b..68e21119791 100644
--- a/src/fstext/rescale-test.cc
+++ b/src/fstext/rescale-test.cc
@@ -20,11 +20,14 @@
 #include "fstext/rescale.h"
 #include "fstext/fstext-utils.h"
 #include "fstext/fst-test-utils.h"
+#include "base/kaldi-math.h"
 // Just check that it compiles, for now.
 
 namespace fst
 {
 
+  using kaldi::Exp;
+  using kaldi::Log;
 
 template<class Arc> void TestComputeTotalWeight() {
   typedef typename Arc::Weight Weight;
@@ -40,7 +43,7 @@ template<class Arc> void TestComputeTotalWeight() {
     fstprinter.Print(&std::cout, "standard output");
   }
 
-  Weight max(-log(2.0));
+  Weight max(-Log(2.0));
   Weight tot = ComputeTotalWeight(*fst, max);
   std::cout << "Total weight is: " << tot.Value() << '\n';
 
@@ -80,7 +83,7 @@ void TestRescaleToStochastic() {
 
   RescaleToStochastic(fst, diff);
   Weight tot = ShortestDistance(*fst),
-      tot2 = ComputeTotalWeight(*fst, Weight(-log(2.0)));
+      tot2 = ComputeTotalWeight(*fst, Weight(-Log(2.0)));
   std::cerr <<  " tot is " << tot<<", tot2 = "<<tot2<<'\n';
   assert(ApproxEqual(tot2, Weight::One(), diff));
 
diff --git a/src/gmm/diag-gmm-test.cc b/src/gmm/diag-gmm-test.cc
index b97cb8d4a4f..d5a956fa5bd 100644
--- a/src/gmm/diag-gmm-test.cc
+++ b/src/gmm/diag-gmm-test.cc
@@ -34,10 +34,10 @@ void InitRandomGmm(DiagGmm *gmm_in) {
   Vector<BaseFloat> weights(num_gauss);
   for (int32 i = 0; i < num_gauss; i++) {
     for (int32 j = 0; j < dim; j++) {
-      inv_vars(i, j) = exp(RandGauss() * (1.0 / (1 + j)));
+      inv_vars(i, j) = Exp(RandGauss() * (1.0 / (1 + j)));
       means(i, j) = RandGauss() * (1.0 / (1 + j));
     }
-    weights(i) = exp(RandGauss());
+    weights(i) = Exp(RandGauss());
   }
   weights.Scale(1.0 / weights.Sum());
   gmm.SetWeights(weights);
@@ -107,7 +107,7 @@ void UnitTestDiagGmm() {
     weights(m) = kaldi::RandUniform();
     for (size_t d= 0; d < dim; d++) {
       means(m, d) = kaldi::RandGauss();
-      vars(m, d) = exp(kaldi::RandGauss()) + 1e-5;
+      vars(m, d) = Exp(kaldi::RandGauss()) + 1e-5;
     }
     tot_weight += weights(m);
   }
@@ -116,10 +116,10 @@ void UnitTestDiagGmm() {
   for (size_t m = 0; m < nMix; m++) {
     weights(m) /= tot_weight;
     for (size_t d= 0; d < dim; d++) {
-      loglikes(m) += -0.5 * (M_LOG_2PI + log(vars(m, d)) + (feat(d) -
+      loglikes(m) += -0.5 * (M_LOG_2PI + Log(vars(m, d)) + (feat(d) -
           means(m, d)) * (feat(d) - means(m, d)) / vars(m, d));
     }
-    loglikes(m) += log(weights(m));
+    loglikes(m) += Log(weights(m));
   }
 
   loglike = loglikes.LogSumExp();
@@ -282,8 +282,8 @@ void UnitTestDiagGmm() {
     gmm1.ComputeGconsts();
 
     std::vector<std::pair<BaseFloat, const DiagGmm*> > vec;
-    vec.push_back(std::make_pair(0.4, (const DiagGmm*)(&gmm1)));
-    vec.push_back(std::make_pair(0.6, (const DiagGmm*)(&gmm1)));
+    vec.push_back(std::make_pair(static_cast<BaseFloat>(0.4), (const DiagGmm*)(&gmm1)));
+    vec.push_back(std::make_pair(static_cast<BaseFloat>(0.6), (const DiagGmm*)(&gmm1)));
     
     DiagGmm gmm2(vec);
 
diff --git a/src/gmm/diag-gmm.cc b/src/gmm/diag-gmm.cc
index d187bc1f8a3..eed5553fc8e 100644
--- a/src/gmm/diag-gmm.cc
+++ b/src/gmm/diag-gmm.cc
@@ -123,9 +123,9 @@ int32 DiagGmm::ComputeGconsts() {
 
   for (int32 mix = 0; mix < num_mix; mix++) {
     KALDI_ASSERT(weights_(mix) >= 0);  // Cannot have negative weights.
-    BaseFloat gc = log(weights_(mix)) + offset;  // May be -inf if weights == 0
+    BaseFloat gc = Log(weights_(mix)) + offset;  // May be -inf if weights == 0
     for (int32 d = 0; d < dim; d++) {
-      gc += 0.5 * log(inv_vars_(mix, d)) - 0.5 * means_invvars_(mix, d)
+      gc += 0.5 * Log(inv_vars_(mix, d)) - 0.5 * means_invvars_(mix, d)
         * means_invvars_(mix, d) / inv_vars_(mix, d);
     }
     // Change sign for logdet because var is inverted. Also, note that
@@ -348,7 +348,7 @@ void DiagGmm::Merge(int32 target_components, std::vector<int32> *history) {
   for (int32 i = 0; i < num_comp; i++) {
     discarded_component[i] = false;
     for (int32 d = 0; d < dim; d++) {
-      logdet(i) += 0.5 * log(inv_vars_(i, d));  // +0.5 because var is inverted
+      logdet(i) += 0.5 * Log(inv_vars_(i, d));  // +0.5 because var is inverted
     }
   }
 
@@ -431,7 +431,7 @@ void DiagGmm::Merge(int32 target_components, std::vector<int32> *history) {
     // Update logdet for merged component
     logdet(max_i) = 0.0;
     for (int32 d = 0; d < dim; d++) {
-      logdet(max_i) += 0.5 * log(inv_vars_(max_i, d));
+      logdet(max_i) += 0.5 * Log(inv_vars_(max_i, d));
       // +0.5 because var is inverted
     }
 
@@ -488,7 +488,7 @@ BaseFloat DiagGmm::merged_components_logdet(BaseFloat w1, BaseFloat w2,
   tmp_var.AddVec2(-1.0, tmp_mean);
   BaseFloat merged_logdet = 0.0;
   for (int32 d = 0; d < dim; d++) {
-    merged_logdet -= 0.5 * log(tmp_var(d));
+    merged_logdet -= 0.5 * Log(tmp_var(d));
     // -0.5 because var is not inverted
   }
   return merged_logdet;
diff --git a/src/gmm/ebw-diag-gmm-test.cc b/src/gmm/ebw-diag-gmm-test.cc
index e80c3698896..bd334921c78 100644
--- a/src/gmm/ebw-diag-gmm-test.cc
+++ b/src/gmm/ebw-diag-gmm-test.cc
@@ -43,7 +43,7 @@ void UnitTestEstimateMmieDiagGmm() {
   for (size_t m = 0; m < nMix; m++) {
     for (size_t d= 0; d < dim; d++) {
       means_f(m, d) = kaldi::RandGauss()*100.0F;
-      vars_f(m, d) = exp(kaldi::RandGauss())*1000.0F+ 1.0F;
+      vars_f(m, d) = Exp(kaldi::RandGauss())*1000.0F+ 1.0F;
     }
     // std::cout << "Gauss " << m << ": Mean = " << means_f.Row(m) << '\n'
     //          << "Vars = " << vars_f.Row(m) << '\n';
@@ -104,7 +104,7 @@ void UnitTestEstimateMmieDiagGmm() {
   Matrix<BaseFloat> means(1, dim), vars(1, dim), invvars(1, dim);
   for (size_t d= 0; d < dim; d++) {
     means(0, d) = kaldi::RandGauss()*100.0F;
-    vars(0, d) = exp(kaldi::RandGauss()) *10.0F + 1e-5F;
+    vars(0, d) = Exp(kaldi::RandGauss()) *10.0F + 1e-5F;
   }
   weights(0) = 1.0F;
   invvars.CopyFromMat(vars);
diff --git a/src/gmm/ebw-diag-gmm.cc b/src/gmm/ebw-diag-gmm.cc
index 3f870a400d0..ef7d83d6ba2 100644
--- a/src/gmm/ebw-diag-gmm.cc
+++ b/src/gmm/ebw-diag-gmm.cc
@@ -78,10 +78,10 @@ static bool EBWUpdateGaussian(
       int32 dim = orig_mean.Dim();
       for (int32 i = 0; i < dim; i++) {
         BaseFloat mean_diff = (*mean)(i) - orig_mean(i);
-        old_auxf += (occ+D) * -0.5 * (log(orig_var(i)) +
+        old_auxf += (occ+D) * -0.5 * (Log(orig_var(i)) +
                                       ((*var)(i) + mean_diff*mean_diff)
                                       / orig_var(i));
-        new_auxf += (occ+D) * -0.5 * (log((*var)(i)) + 1.0);
+        new_auxf += (occ+D) * -0.5 * (Log((*var)(i)) + 1.0);
         
       }
       *auxf_impr = new_auxf - old_auxf;
diff --git a/src/gmm/full-gmm-test.cc b/src/gmm/full-gmm-test.cc
index 50bed26fdb7..66f6a64a554 100644
--- a/src/gmm/full-gmm-test.cc
+++ b/src/gmm/full-gmm-test.cc
@@ -61,7 +61,7 @@ void init_rand_diag_gmm(DiagGmm *gmm) {
     weights(m) = kaldi::RandUniform();
     for (size_t d= 0; d < dim; d++) {
       means(m, d) = kaldi::RandGauss();
-      vars(m, d) = exp(kaldi::RandGauss()) + 1e-5;
+      vars(m, d) = Exp(kaldi::RandGauss()) + 1e-5;
     }
     tot_weight += weights(m);
   }
@@ -156,7 +156,7 @@ UnitTestFullGmm() {
       + VecSpVec(means.Row(m), invcovars[m], means.Row(m))
       + VecSpVec(feat, invcovars[m], feat))
       + VecSpVec(means.Row(m), invcovars[m], feat);
-    loglikes(m) += log(weights(m));
+    loglikes(m) += Log(weights(m));
   }
 
   loglike = loglikes.LogSumExp();
diff --git a/src/gmm/full-gmm.cc b/src/gmm/full-gmm.cc
index c4a5a941408..7851d8648f7 100644
--- a/src/gmm/full-gmm.cc
+++ b/src/gmm/full-gmm.cc
@@ -101,7 +101,7 @@ int32 FullGmm::ComputeGconsts() {
 
   for (int32 mix = 0; mix < num_mix; mix++) {
     KALDI_ASSERT(weights_(mix) >= 0);  // Cannot have negative weights.
-    BaseFloat gc = log(weights_(mix)) + offset;  // May be -inf if weights == 0
+    BaseFloat gc = Log(weights_(mix)) + offset;  // May be -inf if weights == 0
     SpMatrix<BaseFloat> covar(inv_covars_[mix]);
     covar.InvertDouble();
     BaseFloat logdet = covar.LogPosDefDet();
@@ -449,8 +449,8 @@ BaseFloat FullGmm::MergePreselect(int32 target_components,
        removed < num_comp - target_components && !queue.empty(); ) {
     QueueElem qelem = queue.top();
     queue.pop();
-    BaseFloat delta_log_like_old = qelem.first,
-        idx1 = qelem.second.first, idx2 = qelem.second.second;
+    BaseFloat delta_log_like_old = qelem.first;
+    int32 idx1 = qelem.second.first, idx2 = qelem.second.second;
     // the next 3 lines are to handle when components got merged
     // and moved to different indices, but we still want to consider
     // merging their descendants. [descendant = current index where
diff --git a/src/gmm/indirect-diff-diag-gmm.cc b/src/gmm/indirect-diff-diag-gmm.cc
index c41e62e83dc..7311d5f85e9 100644
--- a/src/gmm/indirect-diff-diag-gmm.cc
+++ b/src/gmm/indirect-diff-diag-gmm.cc
@@ -217,7 +217,7 @@ void DoRescalingUpdate(const AccumDiagGmm &old_ml_acc,
       double divergence = 
           0.5 *(((new_model_mean-old_model_mean)*(new_model_mean-old_model_mean) +
                  new_model_var - old_model_var)/old_model_var +
-                log(old_model_var / new_model_var));
+                Log(old_model_var / new_model_var));
       if (divergence < 0.0)
         KALDI_WARN << "Negative divergence " << divergence;
       *tot_divergence += divergence * new_ml_count;
diff --git a/src/gmm/mle-am-diag-gmm-test.cc b/src/gmm/mle-am-diag-gmm-test.cc
index d89ef161ad3..2db7fc889a2 100644
--- a/src/gmm/mle-am-diag-gmm-test.cc
+++ b/src/gmm/mle-am-diag-gmm-test.cc
@@ -115,7 +115,7 @@ void UnitTestMleAmDiagGmm() {
     for (int32 m = 0; m < num_feat_comp; m++) {
       for (int32 d= 0; d < dim; d++) {
         means(m, d) = kaldi::RandGauss();
-        vars(m, d) = exp(kaldi::RandGauss()) + 1e-2;
+        vars(m, d) = Exp(kaldi::RandGauss()) + 1e-2;
       }
     }
     // Now generate random features with those means and variances.
diff --git a/src/gmm/mle-diag-gmm-test.cc b/src/gmm/mle-diag-gmm-test.cc
index 49c8ad2e471..19d44d0dc19 100644
--- a/src/gmm/mle-diag-gmm-test.cc
+++ b/src/gmm/mle-diag-gmm-test.cc
@@ -215,7 +215,7 @@ UnitTestEstimateDiagGmm() {
   for (size_t m = 0; m < nMix; m++) {
     for (size_t d= 0; d < dim; d++) {
       means_f(m, d) = kaldi::RandGauss()*100.0F;
-      vars_f(m, d) = exp(kaldi::RandGauss())*1000.0F+ 1.0F;
+      vars_f(m, d) = Exp(kaldi::RandGauss())*1000.0F+ 1.0F;
     }
 //    std::cout << "Gauss " << m << ": Mean = " << means_f.Row(m) << '\n'
 //        << "Vars = " << vars_f.Row(m) << '\n';
@@ -259,7 +259,7 @@ UnitTestEstimateDiagGmm() {
   Matrix<BaseFloat> means(1, dim), vars(1, dim), invvars(1, dim);
   for (size_t d= 0; d < dim; d++) {
     means(0, d) = kaldi::RandGauss()*100.0F;
-    vars(0, d) = exp(kaldi::RandGauss()) *10.0F + 1e-5F;
+    vars(0, d) = Exp(kaldi::RandGauss()) *10.0F + 1e-5F;
   }
   weights(0) = 1.0F;
   invvars.CopyFromMat(vars);
diff --git a/src/gmm/mle-full-gmm-test.cc b/src/gmm/mle-full-gmm-test.cc
index 60a798e6a39..472db88d501 100644
--- a/src/gmm/mle-full-gmm-test.cc
+++ b/src/gmm/mle-full-gmm-test.cc
@@ -132,7 +132,7 @@ BaseFloat GetLogLikeTest(const FullGmm &gmm,
 
   for (int32 i = 0; i < gmm.NumGauss(); i++) {
     BaseFloat logdet = -(inv_covars[i].LogPosDefDet());
-    BaseFloat log_like = log(gmm.weights()(i))
+    BaseFloat log_like = Log(gmm.weights()(i))
       -0.5 * (gmm.Dim() * M_LOG_2PI + logdet);
     Vector<BaseFloat> offset(feats);
     offset.AddVec(-1.0, means.Row(i));
diff --git a/src/gmm/model-test-common.cc b/src/gmm/model-test-common.cc
index c9324450502..39a257b845b 100644
--- a/src/gmm/model-test-common.cc
+++ b/src/gmm/model-test-common.cc
@@ -93,10 +93,10 @@ void InitRandDiagGmm(int32 dim, int32 num_comp, DiagGmm *gmm) {
   Matrix<BaseFloat> means(num_comp, dim), inv_vars(num_comp, dim);
 
   for (int32 m = 0; m < num_comp; m++) {
-    weights(m) = exp(RandGauss());
+    weights(m) = Exp(RandGauss());
     for (int32 d= 0; d < dim; d++) {
       means(m, d) = RandGauss() / (1 + d);
-      inv_vars(m, d) = exp(RandGauss() / (1 + d)) + 1e-2;
+      inv_vars(m, d) = Exp(RandGauss() / (1 + d)) + 1e-2;
     }
   }
   weights.Scale(1.0 / weights.Sum());
diff --git a/src/hmm/hmm-utils.cc b/src/hmm/hmm-utils.cc
index 9dff30e1570..0303d0d8e50 100644
--- a/src/hmm/hmm-utils.cc
+++ b/src/hmm/hmm-utils.cc
@@ -125,7 +125,7 @@ fst::VectorFst<fst::StdArc> *GetHmmAsFst(
         // no pdf, hence non-estimated probability.
         // [would not happen with normal topology] .  There is no transition-state
         // involved in this case.
-        log_prob = log(entry[hmm_state].transitions[trans_idx].second);
+        log_prob = Log(entry[hmm_state].transitions[trans_idx].second);
         label = 0;
       } else {  // normal probability.
         int32 trans_state =
@@ -225,7 +225,7 @@ GetHmmAsFstSimple(std::vector<int32> phone_window,
         // [would not happen with normal topology] .  There is no transition-state
         // involved in this case.
         KALDI_ASSERT(!is_self_loop);
-        log_prob = log(entry[hmm_state].transitions[trans_idx].second);
+        log_prob = Log(entry[hmm_state].transitions[trans_idx].second);
         label = 0;
       } else {  // normal probability.
         int32 trans_state =
diff --git a/src/hmm/transition-model.cc b/src/hmm/transition-model.cc
index 7b9eba9bea0..f672c9ad54b 100644
--- a/src/hmm/transition-model.cc
+++ b/src/hmm/transition-model.cc
@@ -110,7 +110,7 @@ void TransitionModel::InitializeProbs() {
           "probability [should remove that entry in the topology]";
     if (prob > 1.0)
       KALDI_WARN << "TransitionModel::InitializeProbs, prob greater than one.";
-    log_probs_(trans_id) = log(prob);
+    log_probs_(trans_id) = Log(prob);
   }
   ComputeDerivedOfProbs();
 }
@@ -260,13 +260,13 @@ void TransitionModel::ComputeDerivedOfProbs() {
     if (tid == 0) {  // no self-loop
       non_self_loop_log_probs_(tstate) = 0.0;  // log(1.0)
     } else {
-      BaseFloat self_loop_prob = exp(GetTransitionLogProb(tid)),
+      BaseFloat self_loop_prob = Exp(GetTransitionLogProb(tid)),
           non_self_loop_prob = 1.0 - self_loop_prob;
       if (non_self_loop_prob <= 0.0) {
         KALDI_WARN << "ComputeDerivedOfProbs(): non-self-loop prob is " << non_self_loop_prob;
         non_self_loop_prob = 1.0e-10;  // just so we can continue...
       }
-      non_self_loop_log_probs_(tstate) = log(non_self_loop_prob);  // will be negative.
+      non_self_loop_log_probs_(tstate) = Log(non_self_loop_prob);  // will be negative.
     }
   }
 }
@@ -318,7 +318,7 @@ void TransitionModel::Write(std::ostream &os, bool binary) const {
 }
 
 BaseFloat TransitionModel::GetTransitionProb(int32 trans_id) const {
-  return exp(log_probs_(trans_id));
+  return Exp(log_probs_(trans_id));
 }
 
 BaseFloat TransitionModel::GetTransitionLogProb(int32 trans_id) const {
@@ -376,14 +376,14 @@ void TransitionModel::MleUpdate(const Vector<double> &stats,
         // Compute objf change
         for (int32 tidx = 0; tidx < n; tidx++) {
           if (new_probs(tidx) == cfg.floor) num_floored++;
-          double objf_change = counts(tidx) * (log(new_probs(tidx))
-                                               - log(old_probs(tidx)));
+          double objf_change = counts(tidx) * (Log(new_probs(tidx))
+                                               - Log(old_probs(tidx)));
           objf_impr_sum += objf_change;
         }
         // Commit updated values.
         for (int32 tidx = 0; tidx < n; tidx++) {
           int32 tid = PairToTransitionId(tstate, tidx);
-          log_probs_(tid) = log(new_probs(tidx));
+          log_probs_(tid) = Log(new_probs(tidx));
           if (log_probs_(tid) - log_probs_(tid) != 0.0)
             KALDI_ERR << "Log probs is inf or NaN: error in update or bad stats?";
         }
@@ -435,14 +435,14 @@ void TransitionModel::MapUpdate(const Vector<double> &stats,
             (cfg.tau + tstate_tot);
       // Compute objf change
       for (int32 tidx = 0; tidx < n; tidx++) {
-        double objf_change = counts(tidx) * (log(new_probs(tidx))
-                                             - log(old_probs(tidx)));
+        double objf_change = counts(tidx) * (Log(new_probs(tidx))
+                                             - Log(old_probs(tidx)));
         objf_impr_sum += objf_change;
       }
       // Commit updated values.
       for (int32 tidx = 0; tidx < n; tidx++) {
         int32 tid = PairToTransitionId(tstate, tidx);
-        log_probs_(tid) = log(new_probs(tidx));
+        log_probs_(tid) = Log(new_probs(tidx));
         if (log_probs_(tid) - log_probs_(tid) != 0.0)
           KALDI_ERR << "Log probs is inf or NaN: error in update or bad stats?";
       }
@@ -524,8 +524,8 @@ void TransitionModel::MleUpdateShared(const Vector<double> &stats,
         // Compute objf change
         for (int32 tidx = 0; tidx < n; tidx++) {
           if (new_probs(tidx) == cfg.floor) num_floored++;
-          double objf_change = counts(tidx) * (log(new_probs(tidx))
-                                               - log(old_probs(tidx)));
+          double objf_change = counts(tidx) * (Log(new_probs(tidx))
+                                               - Log(old_probs(tidx)));
           objf_impr_sum += objf_change;
         }
         // Commit updated values.
@@ -535,7 +535,7 @@ void TransitionModel::MleUpdateShared(const Vector<double> &stats,
           int32 tstate = *iter;
           for (int32 tidx = 0; tidx < n; tidx++) {
             int32 tid = PairToTransitionId(tstate, tidx);
-            log_probs_(tid) = log(new_probs(tidx));
+            log_probs_(tid) = Log(new_probs(tidx));
             if (log_probs_(tid) - log_probs_(tid) != 0.0)
               KALDI_ERR << "Log probs is inf or NaN: error in update or bad stats?";
           }
@@ -612,8 +612,8 @@ void TransitionModel::MapUpdateShared(const Vector<double> &stats,
             (pdf_tot + cfg.tau);
       // Compute objf change
       for (int32 tidx = 0; tidx < n; tidx++) {
-        double objf_change = counts(tidx) * (log(new_probs(tidx))
-                                             - log(old_probs(tidx)));
+        double objf_change = counts(tidx) * (Log(new_probs(tidx))
+                                             - Log(old_probs(tidx)));
         objf_impr_sum += objf_change;
       }
       // Commit updated values.
@@ -623,7 +623,7 @@ void TransitionModel::MapUpdateShared(const Vector<double> &stats,
         int32 tstate = *iter;
         for (int32 tidx = 0; tidx < n; tidx++) {
           int32 tid = PairToTransitionId(tstate, tidx);
-          log_probs_(tid) = log(new_probs(tidx));
+          log_probs_(tid) = Log(new_probs(tidx));
           if (log_probs_(tid) - log_probs_(tid) != 0.0)
             KALDI_ERR << "Log probs is inf or NaN: error in update or bad stats?";
         }
diff --git a/src/ivector/logistic-regression-test.cc b/src/ivector/logistic-regression-test.cc
index 8952b78c8df..94a88e1adbc 100644
--- a/src/ivector/logistic-regression-test.cc
+++ b/src/ivector/logistic-regression-test.cc
@@ -117,7 +117,7 @@ void UnitTestTrain() {
   double objf_rand_w = classifier.GetObjfAndGrad(xs_with_prior, ys, 
                                                  xw_rand, &grad, normalizer);
   KALDI_ASSERT(objf_trained > objf_rand_w);
-  KALDI_ASSERT(objf_trained > std::log(1.0 / n_xs));
+  KALDI_ASSERT(objf_trained > Log(1.0 / n_xs));
 }
 }
 
diff --git a/src/ivector/logistic-regression.cc b/src/ivector/logistic-regression.cc
index 4bee6237e44..78fa2746ebb 100644
--- a/src/ivector/logistic-regression.cc
+++ b/src/ivector/logistic-regression.cc
@@ -249,7 +249,7 @@ BaseFloat LogisticRegression::GetObjfAndGrad(
       class_sum += row(cols[j]);
     }
     if (class_sum < 1.0e-20) class_sum = 1.0e-20;
-    raw_objf += std::log(class_sum);
+    raw_objf += Log(class_sum);
     // Iterate over weights for each component. If there are no
     // mixtures each row corresponds to a class.
     for (int32 k = 0; k < weights_.NumRows(); k++) {
diff --git a/src/kws/kws-scoring.cc b/src/kws/kws-scoring.cc
index 87f79ee6cd6..19751f2bb91 100644
--- a/src/kws/kws-scoring.cc
+++ b/src/kws/kws-scoring.cc
@@ -310,7 +310,8 @@ void TwvMetricsOptions::Register(OptionsItf *po) {
   // a mandatory argument, not optional
 }
 
-struct TwvMetricsStats {
+class TwvMetricsStats {
+ public:
   kws_internal::KwScoreStats global_keyword_stats;
   kws_internal::KwStats keyword_stats;
   kws_internal::PerKwSweepStats otwv_sweep_cache;
diff --git a/src/lat/lattice-functions.cc b/src/lat/lattice-functions.cc
index 33090edc5bd..0ea66712eda 100644
--- a/src/lat/lattice-functions.cc
+++ b/src/lat/lattice-functions.cc
@@ -326,18 +326,18 @@ BaseFloat LatticeForwardBackward(const Lattice &lat, Posterior *post,
 
       // The following "if" is an optimization to avoid un-needed exp().
       if (transition_id != 0 || acoustic_like_sum != NULL) {
-        double posterior = exp(alpha[s] + arc_beta - tot_forward_prob);
+        double posterior = Exp(alpha[s] + arc_beta - tot_forward_prob);
 
         if (transition_id != 0) // Arc has a transition-id on it [not epsilon]
           (*post)[state_times[s]].push_back(std::make_pair(transition_id,
-                                                               posterior));
+                                                           static_cast<kaldi::BaseFloat>(posterior)));
         if (acoustic_like_sum != NULL)
           *acoustic_like_sum -= posterior * arc.weight.Value2();
       }
     }
     if (acoustic_like_sum != NULL && f != Weight::Zero()) {
       double final_logprob = - ConvertToCost(f),
-          posterior = exp(alpha[s] + final_logprob - tot_forward_prob);
+          posterior = Exp(alpha[s] + final_logprob - tot_forward_prob);
       *acoustic_like_sum -= posterior * f.Value2();
     }
     beta[s] = this_beta;
@@ -894,12 +894,12 @@ BaseFloat LatticeForwardBackwardMpeVariants(
       beta_smbr[s] += arc_scale * (beta_smbr[arc.nextstate] + frame_acc);
 
       if (transition_id != 0) { // Arc has a transition-id on it [not epsilon]
-        double posterior = exp(alpha[s] + arc_beta - tot_forward_prob);
+        double posterior = Exp(alpha[s] + arc_beta - tot_forward_prob);
         double acc_diff = alpha_smbr[s] + frame_acc + beta_smbr[arc.nextstate]
                                - tot_forward_score;
         double posterior_smbr = posterior * acc_diff;
         (*post)[state_times[s]].push_back(std::make_pair(transition_id,
-                                                             posterior_smbr));
+                                                         static_cast<BaseFloat>(posterior_smbr)));
       }
     }
   }
diff --git a/src/lat/sausages.cc b/src/lat/sausages.cc
index 99746b5f88e..53678efe844 100644
--- a/src/lat/sausages.cc
+++ b/src/lat/sausages.cc
@@ -122,7 +122,7 @@ double MinimumBayesRisk::EditDistance(int32 N, int32 Q,
           alpha_dash_arc(q) = std::min(a1, std::min(a2, a3));
         }
         // line 19:
-        alpha_dash(n, q) += exp(alpha(s_a) + p_a - alpha(n)) * alpha_dash_arc(q);
+        alpha_dash(n, q) += Exp(alpha(s_a) + p_a - alpha(n)) * alpha_dash_arc(q);
       }
     }
   }
@@ -182,7 +182,7 @@ void MinimumBayesRisk::AccStats() {
       beta_dash_arc.SetZero(); // line 19.
       for (int32 q = Q; q >= 1; q--) {
         // line 21:
-        beta_dash_arc(q) += exp(alpha(s_a) + p_a - alpha(n)) * beta_dash(n, q);
+        beta_dash_arc(q) += Exp(alpha(s_a) + p_a - alpha(n)) * beta_dash(n, q);
         switch (static_cast<int>(b_arc[q])) { // lines 22 and 23:
           case 1:
             beta_dash(s_a, q-1) += beta_dash_arc(q);
@@ -210,7 +210,7 @@ void MinimumBayesRisk::AccStats() {
             KALDI_ERR << "Invalid b_arc value"; // error in code.
         }
       }
-      beta_dash_arc(0) += exp(alpha(s_a) + p_a - alpha(n)) * beta_dash(n, 0);
+      beta_dash_arc(0) += Exp(alpha(s_a) + p_a - alpha(n)) * beta_dash(n, 0);
       beta_dash(s_a, 0) += beta_dash_arc(0); // line 26.
     }
   }
diff --git a/src/latbin/lattice-combine.cc b/src/latbin/lattice-combine.cc
index 6e0c04d9e04..be87449827d 100644
--- a/src/latbin/lattice-combine.cc
+++ b/src/latbin/lattice-combine.cc
@@ -81,7 +81,7 @@ bool CompactLatticeNormalize(CompactLattice *clat, BaseFloat weight,
 
   // If exp_weights = false, add to the log AM & LM scores.
   if (!exp_weights)
-    total_backward_cost -= std::log(weight);
+    total_backward_cost -= Log(weight);
   
   for (fst::StateIterator<CompactLattice> sit(*clat); !sit.Done(); sit.Next()) {
     CompactLatticeWeight f = clat->Final(sit.Value());
diff --git a/src/latbin/lattice-confidence.cc b/src/latbin/lattice-confidence.cc
index 8154157268f..b30768a5e6c 100644
--- a/src/latbin/lattice-confidence.cc
+++ b/src/latbin/lattice-confidence.cc
@@ -114,7 +114,7 @@ int main(int argc, char *argv[]) {
         }
         num_done++;
         confidence = std::min(max_output, confidence); // disallow infinity.
-        sum_neg_exp += exp(-confidence); // diagnostic.
+        sum_neg_exp += Exp(-confidence); // diagnostic.
         confidence_writer.Write(key, confidence);
       }
     } else {
@@ -147,7 +147,7 @@ int main(int argc, char *argv[]) {
         }
         num_done++;
         confidence = std::min(max_output, confidence); // disallow infinity.
-        sum_neg_exp += exp(-confidence); // diagnostic.
+        sum_neg_exp += Exp(-confidence); // diagnostic.
         confidence_writer.Write(key, confidence);
       }
     }
@@ -157,7 +157,7 @@ int main(int argc, char *argv[]) {
               << num_empty << " were equivalent to the empty lattice.";
     if (num_done != 0)
       KALDI_LOG << "Average confidence (averaged in negative-log space) is "
-                << -log(sum_neg_exp / num_done);
+                << -Log(sum_neg_exp / num_done);
     
     if (num_same_sentence != 0) {
       KALDI_WARN << num_same_sentence << " lattices had the same sentence on "
diff --git a/src/lm/const-arpa-lm.cc b/src/lm/const-arpa-lm.cc
index 9ab5589ba41..fba7629fda1 100644
--- a/src/lm/const-arpa-lm.cc
+++ b/src/lm/const-arpa-lm.cc
@@ -22,6 +22,7 @@
 #include "lm/const-arpa-lm.h"
 #include "util/stl-utils.h"
 #include "util/text-utils.h"
+#include "base/kaldi-math.h"
 
 namespace kaldi {
 
@@ -396,8 +397,8 @@ void ConstArpaLmBuilder::Read(std::istream &is, bool binary) {
         KALDI_ASSERT(ConvertStringToReal(col[0], &logprob));
         KALDI_ASSERT(ConvertStringToReal(col[1 + cur_order], &backoff_logprob));
         if (natural_base_) {
-          logprob *= log(10);
-          backoff_logprob *= log(10);
+          logprob *= Log(10.0f);
+          backoff_logprob *= Log(10.0f);
         }
        
         // If <ngram_order_> is larger than 1, then we do not create LmState for
diff --git a/src/matrix/matrix-lib-test.cc b/src/matrix/matrix-lib-test.cc
index 5ca5010fc88..cdeba1ec840 100644
--- a/src/matrix/matrix-lib-test.cc
+++ b/src/matrix/matrix-lib-test.cc
@@ -601,7 +601,7 @@ static void UnitTestSimpleForVec() {  // testing some simple operaters on vector
     V1.CopyFromVec(V);
     V1.ApplyExp();
     Real a = V.LogSumExp();
-    V2.Set(exp(V.LogSumExp()));
+    V2.Set(Exp(V.LogSumExp()));
     V1.DivElements(V2);
     V2.CopyFromVec(V);
 
@@ -1144,7 +1144,7 @@ template<typename Real> static void UnitTestDeterminantSign() {
 
     // add in a scaling factor too.
     Real tmp = 1.0 + ((Rand() % 5) * 0.01);
-    Real logdet_factor = dimM * log(tmp);
+    Real logdet_factor = dimM * Log(tmp);
     N.Scale(tmp);
     S.Scale(tmp);
 
@@ -1422,7 +1422,7 @@ template<typename Real> static void UnitTestEig() {
     {  // Check that the eigenvalues match up with the determinant.
       BaseFloat logdet_check = 0.0, logdet = M.LogDet();
       for (MatrixIndexT i = 0; i < dimM ; i++)
-        logdet_check += 0.5 * log(real_eigs(i)*real_eigs(i) + imag_eigs(i)*imag_eigs(i));
+        logdet_check += 0.5 * Log(real_eigs(i)*real_eigs(i) + imag_eigs(i)*imag_eigs(i));
       AssertEqual(logdet_check, logdet);
     }
     Matrix<Real> Pinv(P);
@@ -2305,9 +2305,9 @@ template<typename Real> static void  UnitTestTanh() {
       for (int32 c = 0; c < dimN; c++) {
         Real x = N(r, c);
         if (x > 0.0) {
-          x = -1.0 + 2.0 / (1.0 + exp(-2.0 * x));
+          x = -1.0 + 2.0 / (1.0 + Exp(-2.0 * x));
         } else {
-          x = 1.0 - 2.0 / (1.0 + exp(2.0 * x));
+          x = 1.0 - 2.0 / (1.0 + Exp(2.0 * x));
         }
         N(r, c) = x;
         Real out_diff = P(r, c), in_diff = out_diff * (1.0 - x * x);
@@ -2331,7 +2331,7 @@ template<typename Real> static void  UnitTestSigmoid() {
     for(int32 r = 0; r < dimM; r++) {
       for (int32 c = 0; c < dimN; c++) {
         Real x = N(r, c),
-            y = 1.0 / (1 + exp(-x));
+            y = 1.0 / (1 + Exp(-x));
         N(r, c) = y;
         Real out_diff = P(r, c), in_diff = out_diff * y * (1.0 - y);
         Q(r, c) = in_diff;
@@ -2356,7 +2356,7 @@ template<typename Real> static void  UnitTestSoftHinge() {
         Real x = M(r, c);
         Real &y = N(r, c);
         if (x > 10.0) y = x;
-        else y = log(1.0 + exp(x));
+        else y = Log1p(Exp(x));
       }
     }
     O.SoftHinge(M);
@@ -2395,7 +2395,7 @@ template<typename Real> static void  UnitTestSimple() {
     {
       Vector<Real> V2(V);
       for (MatrixIndexT i = 0; i < V2.Dim(); i++)
-        V2(i) = exp(V2(i));
+        V2(i) = Exp(V2(i));
       V.ApplyExp();
       AssertEqual(V, V2);
     }
@@ -2403,7 +2403,7 @@ template<typename Real> static void  UnitTestSimple() {
       Matrix<Real> N2(N), N3(N);
       for (MatrixIndexT i = 0; i < N.NumRows(); i++)
         for (MatrixIndexT j = 0; j < N.NumCols(); j++)
-          N2(i, j) = exp(N2(i, j));
+          N2(i, j) = Exp(N2(i, j));
       N3.ApplyExp();
       AssertEqual(N2, N3);
     }
@@ -3121,7 +3121,7 @@ template<typename Real> static void UnitTestLbfgs() {
       Vector<Real> dlogf_dx(v); //  derivative of log(f) w.r.t. x.
       dlogf_dx.AddSpVec(-1.0, S, x, 1.0);
       KALDI_VLOG(2) << "Gradient magnitude is " << dlogf_dx.Norm(2.0);
-      Real f = exp(c * logf);
+      Real f = Exp(c * logf);
       Vector<Real> df_dx(dlogf_dx);
       df_dx.Scale(f * c); // comes from derivative of the exponential function.
       f *= sign;
diff --git a/src/nnet/nnet-component-test.cc b/src/nnet/nnet-component-test.cc
index 810a4dc702a..dcd40cfd81f 100644
--- a/src/nnet/nnet-component-test.cc
+++ b/src/nnet/nnet-component-test.cc
@@ -292,7 +292,6 @@ namespace nnet1 {
     AssertEqual(mat_in_diff, mat_in_diff_ref);
 
     delete c;
-	
   }
 
 } // namespace nnet1
diff --git a/src/nnet/nnet-rbm.h b/src/nnet/nnet-rbm.h
index 69ebefc7721..d1b49cc9556 100644
--- a/src/nnet/nnet-rbm.h
+++ b/src/nnet/nnet-rbm.h
@@ -166,7 +166,7 @@ class Rbm : public RbmBase {
       for(int32 d = 0; d < p.Dim(); d++) {
         if(p(d) < 0.0001) p(d) = 0.0001;
         if(p(d) > 0.9999) p(d) = 0.9999;
-        logit_p(d) = log(p(d)) - log(1.0 - p(d));
+        logit_p(d) = Log(p(d)) - Log(1.0 - p(d));
       }
       vis_bias_ = logit_p;
       KALDI_ASSERT(vis_bias_.Dim() == InputDim());
diff --git a/src/nnet2/mixup-nnet.cc b/src/nnet2/mixup-nnet.cc
index 7f447faf99c..a7d3723d08e 100644
--- a/src/nnet2/mixup-nnet.cc
+++ b/src/nnet2/mixup-nnet.cc
@@ -199,7 +199,7 @@ void SoftmaxComponent::MixUp(int32 num_mixtures,
       rand.SetRandn();
       cur_vec.AddVec(perturb_stddev, rand);
       new_vec.AddVec(-perturb_stddev, rand);
-      this_new_bias_term(max_index) += log(0.5);
+      this_new_bias_term(max_index) += Log(0.5);
       this_new_bias_term(new_index) = this_new_bias_term(max_index);
     }
     old_offset += this_old_dim;
diff --git a/src/nnet2/nnet-component.cc b/src/nnet2/nnet-component.cc
index 6a18d309012..4b9b33d3ad9 100644
--- a/src/nnet2/nnet-component.cc
+++ b/src/nnet2/nnet-component.cc
@@ -1032,7 +1032,7 @@ void LogSoftmaxComponent::Propagate(const ChunkInfo &in_info,
   out->ApplyLogSoftMaxPerRow(in);
 
   // Just to be consistent with SoftmaxComponent::Propagate()
-  out->ApplyFloor(log(1.0e-20));
+  out->ApplyFloor(Log(1.0e-20));
 }
 
 void LogSoftmaxComponent::Backprop(const ChunkInfo &in_info,
diff --git a/src/nnet2/nnet-compute-discriminative.cc b/src/nnet2/nnet-compute-discriminative.cc
index 97fdca942a3..c844d92a55b 100644
--- a/src/nnet2/nnet-compute-discriminative.cc
+++ b/src/nnet2/nnet-compute-discriminative.cc
@@ -251,7 +251,7 @@ void NnetDiscriminativeUpdater::LatticeComputations() {
       num_floored++;
     }
     int32 pdf_id = requested_indexes[index].second;
-    BaseFloat pseudo_loglike = log(post / priors(pdf_id)) * opts_.acoustic_scale;
+    BaseFloat pseudo_loglike = Log(post / priors(pdf_id)) * opts_.acoustic_scale;
     KALDI_ASSERT(!KALDI_ISINF(pseudo_loglike) && !KALDI_ISNAN(pseudo_loglike));
     answers[index] = pseudo_loglike;
   }
diff --git a/src/nnet2/nnet-compute.cc b/src/nnet2/nnet-compute.cc
index 5d939d73712..84c98bb284a 100644
--- a/src/nnet2/nnet-compute.cc
+++ b/src/nnet2/nnet-compute.cc
@@ -125,7 +125,7 @@ BaseFloat NnetComputer::ComputeLastLayerDeriv(const Posterior &pdf_post,
       KALDI_ASSERT(label >= 0 && label < num_pdfs);
       BaseFloat this_prob = last_layer_output(i, label);
       KALDI_ASSERT(this_prob > 0.99e-20); // We floored to 1.0e-20 in SoftmaxLayer.
-      tot_objf += weight * log(this_prob);
+      tot_objf += weight * Log(this_prob);
       tot_weight += weight;
       (*deriv)(i, label) += weight / this_prob; // could be "=", assuming the
       // labels are all distinct.
diff --git a/src/nnet2/nnet-precondition-online-test.cc b/src/nnet2/nnet-precondition-online-test.cc
index 63b52711ae0..aa7e8744963 100644
--- a/src/nnet2/nnet-precondition-online-test.cc
+++ b/src/nnet2/nnet-precondition-online-test.cc
@@ -115,7 +115,7 @@ void OnlinePreconditionerSimple::Init(const MatrixBase<double> &R0) {
 
 BaseFloat OnlinePreconditionerSimple::Eta(int32 N) const {
   KALDI_ASSERT(num_samples_history_ > 0.0);
-  return 1.0 - exp(-N / num_samples_history_);
+  return 1.0 - Exp(-N / num_samples_history_);
 }
 
 
diff --git a/src/nnet2/nnet-precondition-online.cc b/src/nnet2/nnet-precondition-online.cc
index fea7e19e349..01af3e276d2 100644
--- a/src/nnet2/nnet-precondition-online.cc
+++ b/src/nnet2/nnet-precondition-online.cc
@@ -389,7 +389,7 @@ void OnlinePreconditioner::PreconditionDirectionsInternal(
 
 BaseFloat OnlinePreconditioner::Eta(int32 N) const {
   KALDI_ASSERT(num_samples_history_ > 0.0);
-  return 1.0 - exp(-N / num_samples_history_);
+  return 1.0 - Exp(-N / num_samples_history_);
 }
 
 void OnlinePreconditioner::ComputeWt1(int32 N,
diff --git a/src/nnet2bin/nnet-adjust-priors.cc b/src/nnet2bin/nnet-adjust-priors.cc
index 6ff497c20a0..b4b04b83c6c 100644
--- a/src/nnet2bin/nnet-adjust-priors.cc
+++ b/src/nnet2bin/nnet-adjust-priors.cc
@@ -41,7 +41,7 @@ BaseFloat KlDivergence(const Vector<BaseFloat> &p,
 
   for (int32 i = 0; i < p.Dim(); i++) {
     BaseFloat p_prob = p(i) / sum_p, q_prob = q(i) / sum_q;
-    ans += p_prob * log(p_prob / q_prob);
+    ans += p_prob * Log(p_prob / q_prob);
   }
   return ans;
 }
diff --git a/src/nnet2bin/nnet-modify-learning-rates.cc b/src/nnet2bin/nnet-modify-learning-rates.cc
index 312fe2c3337..55cab630080 100644
--- a/src/nnet2bin/nnet-modify-learning-rates.cc
+++ b/src/nnet2bin/nnet-modify-learning-rates.cc
@@ -161,7 +161,7 @@ int main(int argc, char *argv[]) {
     // Gets target geometric mean.
     BaseFloat target_geometric_mean = 0.0; 
     if (average_learning_rate == 0.0) {
-      target_geometric_mean = exp(cur_nnet_learning_rates.SumLog()
+      target_geometric_mean = Exp(cur_nnet_learning_rates.SumLog()
                                   / static_cast<BaseFloat>(num_updatable));
     } else {
       target_geometric_mean = average_learning_rate;
@@ -177,7 +177,7 @@ int main(int argc, char *argv[]) {
     nnet_learning_rates(num_updatable - 1) *= last_layer_factor;
     KALDI_ASSERT(first_layer_factor > 0.0);
     nnet_learning_rates(0) *= first_layer_factor;
-    BaseFloat cur_geometric_mean = exp(nnet_learning_rates.SumLog()
+    BaseFloat cur_geometric_mean = Exp(nnet_learning_rates.SumLog()
                                  / static_cast<BaseFloat>(num_updatable));
     nnet_learning_rates.Scale(target_geometric_mean / cur_geometric_mean);
     KALDI_LOG << "New learning rates for current model per layer are "
diff --git a/src/nnetbin/cuda-gpu-available.cc b/src/nnetbin/cuda-gpu-available.cc
index ac684149243..86ecf0d8b97 100644
--- a/src/nnetbin/cuda-gpu-available.cc
+++ b/src/nnetbin/cuda-gpu-available.cc
@@ -62,6 +62,7 @@ int main(int argc, char *argv[]) try {
             << std::endl
             << "### - Check with NVidia web that your 'display driver' and 'CUDA toolkit' is not too old." 
             << std::endl;
+  static_cast<void>(e);  //To avoid "unreferenced local variable"
   return 1;
 }
 
diff --git a/src/online/online-audio-source.cc b/src/online/online-audio-source.cc
index f3ab5d31b12..a08f0c4aa6f 100644
--- a/src/online/online-audio-source.cc
+++ b/src/online/online-audio-source.cc
@@ -55,7 +55,7 @@ OnlinePaSource::OnlinePaSource(const uint32 timeout,
       report_interval_(report_interval), nread_calls_(0),
       noverflows_(0), samples_lost_(0) {
   using namespace std;
-  int32 rb_bits = static_cast<int32>(ceil(log(rb_size) / log(2)));
+  int32 rb_bits = static_cast<int32>(ceil(Log(rb_size) / Log(2)));
   if (rb_bits > 30) // ok, this limit is somewhat arbitrary
     throw invalid_argument("PortAudio ring buffer too large!");
   rb_size_ = 1 << rb_bits;
diff --git a/src/online2/online-gmm-decodable.cc b/src/online2/online-gmm-decodable.cc
index 0134f41e3a0..20a23858adb 100644
--- a/src/online2/online-gmm-decodable.cc
+++ b/src/online2/online-gmm-decodable.cc
@@ -31,7 +31,7 @@ DecodableDiagGmmScaledOnline::DecodableDiagGmmScaledOnline(
       feat_dim_(input_feats->Dim()), cur_feats_(feat_dim_),
       cur_frame_(-1) {
   int32 num_pdfs = trans_model_.NumPdfs();
-  cache_.resize(num_pdfs, std::pair<int32,BaseFloat>(-1, 0.0));
+  cache_.resize(num_pdfs, std::pair<int32,BaseFloat>(-1, 0.0f));
 }
 
 void DecodableDiagGmmScaledOnline::CacheFrame(int32 frame) {
diff --git a/src/sgmm/am-sgmm.cc b/src/sgmm/am-sgmm.cc
index 0416a9c2391..1cd7c6a2b62 100644
--- a/src/sgmm/am-sgmm.cc
+++ b/src/sgmm/am-sgmm.cc
@@ -700,7 +700,7 @@ void AmSgmm::ComputeNormalizers() {
   KALDI_LOG << "Entropy of weights in substates is "
             << (entropy_sum / entropy_count) << " over " << entropy_count
             << " substates, equivalent to perplexity of "
-            << (exp(entropy_sum /entropy_count));
+            << (Exp(entropy_sum /entropy_count));
   KALDI_LOG << "Done computing normalizers";
 }
 
@@ -709,7 +709,7 @@ void AmSgmm::ComputeNormalizersInternal(int32 num_threads, int32 thread,
                                         int32 *entropy_count,
                                         double *entropy_sum) {
 
-  BaseFloat DLog2pi = FeatureDim() * log(2 * M_PI);
+  BaseFloat DLog2pi = FeatureDim() * Log(2 * M_PI);
   Vector<BaseFloat> log_det_Sigma(NumGauss());
 
   for (int32 i = 0; i < NumGauss(); i++) {
@@ -740,7 +740,7 @@ void AmSgmm::ComputeNormalizersInternal(int32 num_threads, int32 thread,
       {  // DIAGNOSTIC CODE
         (*entropy_count)++;
         for (int32 i = 0; i < NumGauss(); i++) {
-          (*entropy_sum) -= log_w_jm(m, i) * exp(log_w_jm(m, i));
+          (*entropy_sum) -= log_w_jm(m, i) * Exp(log_w_jm(m, i));
         }
       }
     }      
@@ -753,7 +753,7 @@ void AmSgmm::ComputeNormalizersInternal(int32 num_threads, int32 thread,
       for (int32 m = 0; m < NumSubstates(j); m++) {
         // mu_{jmi} * \Sigma_{i}^{-1} * mu_{jmi}
         BaseFloat mu_SigmaInv_mu = VecVec(mu_jmi.Row(m), SigmaInv_mu.Row(m));
-        BaseFloat logc = log(c_[j](m));
+        BaseFloat logc = Log(c_[j](m));
 
         // Suggestion: Both mu_jmi and SigmaInv_mu could
         // have been computed at once for i,
@@ -793,7 +793,7 @@ void AmSgmm::ComputeNormalizersNormalized(
   }
 
   KALDI_LOG << "Computing normalizers [normalized]";
-  BaseFloat DLog2pi = FeatureDim() * log(2 * M_PI);
+  BaseFloat DLog2pi = FeatureDim() * Log(2 * M_PI);
   Vector<BaseFloat> mu_jmi(FeatureDim());
   Vector<BaseFloat> SigmaInv_mu(FeatureDim());
   Vector<BaseFloat> log_det_Sigma(NumGauss());
@@ -814,7 +814,7 @@ void AmSgmm::ComputeNormalizersNormalized(
 
     n_[j].Resize(NumGauss(), NumSubstates(j));
     for (int32 m = 0; m < NumSubstates(j); m++) {
-      BaseFloat logc = log(c_[j](m));
+      BaseFloat logc = Log(c_[j](m));
 
       // (in logs): w_jm = softmax([w_{k1}^T ... w_{kD}^T] * v_{jkm}) eq.(7)
       log_w_jm.AddMatVec(1.0, w_, kNoTrans, v_[j].Row(m), 0.0);
@@ -824,8 +824,8 @@ void AmSgmm::ComputeNormalizersNormalized(
         const std::vector<int32> &this_set(normalize_sets[n]);
         double sum = 0.0;
         for (int32 p = 0; p < this_set.size(); p++)
-          sum += exp(log_w_jm(this_set[p]));
-        double offset = -log(sum);  // add "offset", to normalize weights.
+          sum += Exp(log_w_jm(this_set[p]));
+        double offset = -Log(sum);  // add "offset", to normalize weights.
         for (int32 p = 0; p < this_set.size(); p++)
           log_w_jm(this_set[p]) += offset;
       }
diff --git a/src/sgmm/estimate-am-sgmm-multi-test.cc b/src/sgmm/estimate-am-sgmm-multi-test.cc
index 6222fb1b900..883934a3ce0 100644
--- a/src/sgmm/estimate-am-sgmm-multi-test.cc
+++ b/src/sgmm/estimate-am-sgmm-multi-test.cc
@@ -22,10 +22,13 @@
 #include "sgmm/estimate-am-sgmm.h"
 #include "sgmm/estimate-am-sgmm-multi.h"
 #include "util/kaldi-io.h"
+#include "base/kaldi-math.h"
 
 using kaldi::AmSgmm;
 using kaldi::MleAmSgmmAccs;
 using kaldi::BaseFloat;
+using kaldi::Exp;
+
 namespace ut = kaldi::unittest;
 
 // Tests the MleAmSgmmUpdaterMulti (and MleAmSgmmGlobalAccs) classes.
@@ -122,7 +125,7 @@ void UnitTestEstimateSgmm() {
     for (int32 m = 0; m < num_feat_comp; ++m) {
       for (int32 d= 0; d < dim; d++) {
         means(m, d) = kaldi::RandGauss();
-        vars(m, d) = exp(kaldi::RandGauss()) + 1e-2;
+        vars(m, d) = Exp(kaldi::RandGauss()) + 1e-2;
       }
     }
     // Now generate random features with those means and variances.
diff --git a/src/sgmm/estimate-am-sgmm-test.cc b/src/sgmm/estimate-am-sgmm-test.cc
index 35ba19941ec..a671b7fcb74 100644
--- a/src/sgmm/estimate-am-sgmm-test.cc
+++ b/src/sgmm/estimate-am-sgmm-test.cc
@@ -22,10 +22,13 @@
 #include "sgmm/am-sgmm.h"
 #include "sgmm/estimate-am-sgmm.h"
 #include "util/kaldi-io.h"
+#include "base/kaldi-math.h"
 
 using kaldi::AmSgmm;
 using kaldi::MleAmSgmmAccs;
 using kaldi::BaseFloat;
+using kaldi::Exp;
+
 namespace ut = kaldi::unittest;
 
 // Tests the Read() and Write() methods for the accumulators, in both binary
@@ -137,7 +140,7 @@ void UnitTestEstimateSgmm() {
     for (int32 m = 0; m < num_feat_comp; m++) {
       for (int32 d= 0; d < dim; d++) {
         means(m, d) = kaldi::RandGauss();
-        vars(m, d) = exp(kaldi::RandGauss()) + 1e-2;
+        vars(m, d) = Exp(kaldi::RandGauss()) + 1e-2;
       }
     }
     // Now generate random features with those means and variances.
diff --git a/src/sgmm/estimate-am-sgmm.cc b/src/sgmm/estimate-am-sgmm.cc
index b9c448af071..1e95e6b281c 100644
--- a/src/sgmm/estimate-am-sgmm.cc
+++ b/src/sgmm/estimate-am-sgmm.cc
@@ -1267,7 +1267,7 @@ void MleAmSgmmUpdater::ComputeMPrior(AmSgmm *model) {
     Matrix<double> MDiff(Ddim, Sdim);
     for (int32 iter = 0; iter < update_options_.map_M_prior_iters; iter++) {
       { // diagnostic block.
-        double prior_like = -0.5 * nGaussians * (Ddim * Sdim * log(2 * M_PI)
+        double prior_like = -0.5 * nGaussians * (Ddim * Sdim * Log(2 * M_PI)
                 + Sdim * (-model->row_cov_inv_.LogPosDefDet())
                 + Ddim * (-model->col_cov_inv_.LogPosDefDet()));
         for (int32 i = 0; i < nGaussians; i++) {
@@ -1639,7 +1639,7 @@ double MleAmSgmmUpdater::UpdateWSequential(
           double this_unnormalized_weight = VecVec(w_i, model->v_[j].Row(m));
           double normalizer = weight_tots[j](m);
           double this_log_w = this_unnormalized_weight - normalizer,
-              this_w = exp(this_log_w),
+              this_w = Exp(this_log_w),
               substate_count = gamma_jm[j](m),
               this_count = accs.gamma_[j](m, i);
 
@@ -2000,7 +2000,7 @@ double MleAmSgmmUpdater::UpdateSubstateWeights(
         cur_weight = 1.0e-10;  // future work(arnab): remove magic numbers
       }
       model->c_[j](m) = smoothed_occs(m) / gamma_j_sm;
-      objf_impr += log(model->c_[j](m) / cur_weight) * occs(m);
+      objf_impr += Log(model->c_[j](m) / cur_weight) * occs(m);
     }
     tot_gamma += gamma_j_(j);
   }
diff --git a/src/sgmm/fmllr-sgmm-test.cc b/src/sgmm/fmllr-sgmm-test.cc
index 8a44d217f9a..c9239d5740c 100644
--- a/src/sgmm/fmllr-sgmm-test.cc
+++ b/src/sgmm/fmllr-sgmm-test.cc
@@ -20,6 +20,7 @@
 
 #include <vector>
 
+#include "base/kaldi-math.h"
 #include "gmm/model-test-common.h"
 #include "sgmm/am-sgmm.h"
 #include "sgmm/fmllr-sgmm.h"
@@ -30,6 +31,8 @@ using kaldi::int32;
 using kaldi::BaseFloat;
 using kaldi::Vector;
 using kaldi::Matrix;
+using kaldi::Exp;
+
 namespace ut = kaldi::unittest;
 
 void ApplyFmllrXform(const kaldi::VectorBase<BaseFloat> &in,
@@ -206,7 +209,7 @@ void TestSgmmFmllr() {
     for (int32 m = 0; m < num_feat_comp; m++) {
       for (int32 d= 0; d < dim; d++) {
         means(m, d) = kaldi::RandGauss();
-        vars(m, d) = exp(kaldi::RandGauss()) + 1e-2;
+        vars(m, d) = Exp(kaldi::RandGauss()) + 1e-2;
       }
     }
     // Now generate random features with those means and variances.
diff --git a/src/sgmm/sgmm-clusterable.cc b/src/sgmm/sgmm-clusterable.cc
index 0f07d9ef51a..f49f4f993f2 100644
--- a/src/sgmm/sgmm-clusterable.cc
+++ b/src/sgmm/sgmm-clusterable.cc
@@ -77,7 +77,7 @@ BaseFloat SgmmClusterable::Objf() const {
       if (prob > 0.0) { // Note: prob could be zero due to underflow-- this
         // happened! [we can get tiny values due to floating-point roundoff
         // while subtracting clusterable objects].
-        ans += gamma * log(gamma / tot_gamma);
+        ans += gamma * Log(gamma / tot_gamma);
       }        
     }
     tot_gamma2 += gamma;
diff --git a/src/sgmm2/am-sgmm2.cc b/src/sgmm2/am-sgmm2.cc
index 9486366abc2..70f5b72847e 100644
--- a/src/sgmm2/am-sgmm2.cc
+++ b/src/sgmm2/am-sgmm2.cc
@@ -559,7 +559,7 @@ BaseFloat AmSgmm2::LogLikelihood(const Sgmm2PerFrameDerivedVars &per_frame_vars,
   }
   
   BaseFloat log_like = substate_cache.remaining_log_like
-      + log(VecVec(substate_cache.likes, c_[j2]));
+      + Log(VecVec(substate_cache.likes, c_[j2]));
 
   if (random_test)
     KALDI_ASSERT(ApproxEqual(pdf_cache.log_like, log_like));
@@ -589,7 +589,7 @@ AmSgmm2::ComponentPosteriors(const Sgmm2PerFrameDerivedVars &per_frame_vars,
 
   BaseFloat tot_like = post->Sum();
   KALDI_ASSERT(tot_like != 0.0); // note: not valid to have zero weights.
-  loglike += log(tot_like);
+  loglike += Log(tot_like);
   post->Scale(1.0 / tot_like); // so "post" now sums to one, and "loglike"
   // contains the correct log-likelihood of the data given the pdf.
 
@@ -859,7 +859,7 @@ void AmSgmm2::ComputeNormalizers() {
   KALDI_LOG << "Entropy of weights in substates is "
             << (entropy_sum / entropy_count) << " over " << entropy_count
             << " substates, equivalent to perplexity of "
-            << (exp(entropy_sum /entropy_count));
+            << (Exp(entropy_sum /entropy_count));
   KALDI_LOG << "Done computing normalizers";
 }
 
@@ -868,7 +868,7 @@ void AmSgmm2::ComputeNormalizersInternal(int32 num_threads, int32 thread,
                                          int32 *entropy_count,
                                          double *entropy_sum) {
 
-  BaseFloat DLog2pi = FeatureDim() * log(2 * M_PI);
+  BaseFloat DLog2pi = FeatureDim() * Log(2 * M_PI);
   Vector<BaseFloat> log_det_Sigma(NumGauss());
 
   for (int32 i = 0; i < NumGauss(); i++) {
@@ -902,7 +902,7 @@ void AmSgmm2::ComputeNormalizersInternal(int32 num_threads, int32 thread,
       {  // DIAGNOSTIC CODE
         (*entropy_count)++;
         for (int32 i = 0; i < NumGauss(); i++) {
-          (*entropy_sum) -= log_w_jm(m, i) * exp(log_w_jm(m, i));
+          (*entropy_sum) -= log_w_jm(m, i) * Exp(log_w_jm(m, i));
         }
       }
     }      
@@ -952,7 +952,7 @@ BaseFloat AmSgmm2::GetDjms(int32 j1, int32 m,
     log_d.AddMatVec(1.0, w_jmi_[j1], kNoTrans, spk_vars->b_is, 0.0);
     log_d.ApplyLog();    
   }
-  return exp(log_d(m));
+  return Exp(log_d(m));
 }
 
 
diff --git a/src/sgmm2/estimate-am-sgmm2-test.cc b/src/sgmm2/estimate-am-sgmm2-test.cc
index 945d68255cb..9093c1108db 100644
--- a/src/sgmm2/estimate-am-sgmm2-test.cc
+++ b/src/sgmm2/estimate-am-sgmm2-test.cc
@@ -19,6 +19,7 @@
 // See the Apache 2 License for the specific language governing permissions and
 // limitations under the License.
 
+#include "base/kaldi-math.h"
 #include "gmm/model-test-common.h"
 #include "sgmm2/am-sgmm2.h"
 #include "sgmm2/estimate-am-sgmm2.h"
@@ -28,6 +29,8 @@ using kaldi::AmSgmm2;
 using kaldi::MleAmSgmm2Accs;
 using kaldi::int32;
 using kaldi::BaseFloat;
+using kaldi::Exp;
+
 namespace ut = kaldi::unittest;
 
 // Tests the Read() and Write() methods for the accumulators, in both binary
@@ -143,7 +146,7 @@ void UnitTestEstimateSgmm2() {
     for (int32 m = 0; m < num_feat_comp; m++) {
       for (int32 d= 0; d < dim; d++) {
         means(m, d) = kaldi::RandGauss();
-        vars(m, d) = exp(kaldi::RandGauss()) + 1e-2;
+        vars(m, d) = Exp(kaldi::RandGauss()) + 1e-2;
       }
     }
     // Now generate random features with those means and variances.
diff --git a/src/sgmm2/estimate-am-sgmm2.cc b/src/sgmm2/estimate-am-sgmm2.cc
index 3f2bae3945c..b7a6fe6841c 100644
--- a/src/sgmm2/estimate-am-sgmm2.cc
+++ b/src/sgmm2/estimate-am-sgmm2.cc
@@ -1108,7 +1108,7 @@ void MleAmSgmm2Updater::ComputeMPrior(AmSgmm2 *model) {
     Matrix<double> MDiff(Ddim, Sdim);
     for (int32 iter = 0; iter < options_.map_M_prior_iters; iter++) {
       { // diagnostic block.
-        double prior_like = -0.5 * nGaussians * (Ddim * Sdim * log(2 * M_PI)
+        double prior_like = -0.5 * nGaussians * (Ddim * Sdim * Log(2 * M_PI)
                 + Sdim * (-model->row_cov_inv_.LogPosDefDet())
                 + Ddim * (-model->col_cov_inv_.LogPosDefDet()));
         for (int32 i = 0; i < nGaussians; i++) {
@@ -1695,7 +1695,7 @@ double MleAmSgmm2Updater::UpdateSubstateWeights(
         cur_weight = 1.0e-10;  // future work(arnab): remove magic numbers
       }
       model->c_[j2](m) = smoothed_occs(m) / gamma_j_sm;
-      objf_impr += log(model->c_[j2](m) / cur_weight) * occs(m);
+      objf_impr += Log(model->c_[j2](m) / cur_weight) * occs(m);
     }
   }
   KALDI_LOG << "**Overall objf impr for c is " << (objf_impr/tot_gamma)
diff --git a/src/sgmm2/fmllr-sgmm2-test.cc b/src/sgmm2/fmllr-sgmm2-test.cc
index 3301cf48780..ede25d76c68 100644
--- a/src/sgmm2/fmllr-sgmm2-test.cc
+++ b/src/sgmm2/fmllr-sgmm2-test.cc
@@ -20,6 +20,7 @@
 
 #include <vector>
 
+#include "base/kaldi-math.h"
 #include "gmm/model-test-common.h"
 #include "sgmm2/am-sgmm2.h"
 #include "sgmm2/fmllr-sgmm2.h"
@@ -30,6 +31,8 @@ using kaldi::int32;
 using kaldi::BaseFloat;
 using kaldi::Vector;
 using kaldi::Matrix;
+using kaldi::Exp;
+
 namespace ut = kaldi::unittest;
 
 void ApplyFmllrXform(const kaldi::VectorBase<BaseFloat> &in,
@@ -217,7 +220,7 @@ void TestSgmm2Fmllr() {
     for (int32 m = 0; m < num_feat_comp; m++) {
       for (int32 d= 0; d < dim; d++) {
         means(m, d) = kaldi::RandGauss();
-        vars(m, d) = exp(kaldi::RandGauss()) + 1e-2;
+        vars(m, d) = Exp(kaldi::RandGauss()) + 1e-2;
       }
     }
     // Now generate random features with those means and variances.
diff --git a/src/transform/decodable-am-diag-gmm-regtree.cc b/src/transform/decodable-am-diag-gmm-regtree.cc
index 553d59a951e..536fb8ed1bc 100644
--- a/src/transform/decodable-am-diag-gmm-regtree.cc
+++ b/src/transform/decodable-am-diag-gmm-regtree.cc
@@ -124,9 +124,9 @@ static void ComputeGconsts(const VectorBase<BaseFloat> &weights,
 
   for (int32 gauss = 0; gauss < num_gauss; gauss++) {
     KALDI_ASSERT(weights(gauss) >= 0);  // Cannot have negative weights.
-    BaseFloat gc = log(weights(gauss)) + offset;  // May be -inf if weights == 0
+    BaseFloat gc = Log(weights(gauss)) + offset;  // May be -inf if weights == 0
     for (int32 d = 0; d < dim; d++) {
-      gc += 0.5 * log(inv_vars(gauss, d)) - 0.5 * means(gauss, d)
+      gc += 0.5 * Log(inv_vars(gauss, d)) - 0.5 * means(gauss, d)
         * means(gauss, d) * inv_vars(gauss, d);  // diff from DiagGmm version.
     }
 
diff --git a/src/transform/fmllr-diag-gmm-test.cc b/src/transform/fmllr-diag-gmm-test.cc
index 5788441a5c6..00291fd5fe3 100644
--- a/src/transform/fmllr-diag-gmm-test.cc
+++ b/src/transform/fmllr-diag-gmm-test.cc
@@ -34,10 +34,10 @@ void InitRandomGmm (DiagGmm *gmm_in) {
   Vector<BaseFloat> weights(num_gauss);
   for (int32 i = 0; i < num_gauss; i++) {
     for (int32 j = 0; j < dim; j++) {
-      inv_vars(i, j) = exp(RandGauss() * (1.0 / (1 + j)));
+      inv_vars(i, j) = Exp(RandGauss() * (1.0 / (1 + j)));
       means(i, j) = RandGauss() * (1.0 / (1 + j));
     }
-    weights(i) = exp(RandGauss());
+    weights(i) = Exp(RandGauss());
   }
   weights.Scale(1.0 / weights.Sum());
   gmm.SetWeights(weights);
diff --git a/src/transform/fmllr-diag-gmm.cc b/src/transform/fmllr-diag-gmm.cc
index ce5c13432f1..d4ab289ad9d 100644
--- a/src/transform/fmllr-diag-gmm.cc
+++ b/src/transform/fmllr-diag-gmm.cc
@@ -221,9 +221,9 @@ void FmllrInnerUpdate(SpMatrix<double> &inv_G,
   double discr = std::sqrt(e2 * e2 + 4 * e1 * beta);
   double alpha1 = (-e2 + discr) / (2 * e1);
   double alpha2 = (-e2 - discr) / (2 * e1);
-  double auxf1 = beta * std::log(std::abs(alpha1 * e1 + e2)) -
+  double auxf1 = beta * Log(std::abs(alpha1 * e1 + e2)) -
       0.5 * alpha1 * alpha1 * e1;
-  double auxf2 = beta * std::log(std::abs(alpha2 * e1 + e2)) -
+  double auxf2 = beta * Log(std::abs(alpha2 * e1 + e2)) -
       0.5 * alpha2 * alpha2 * e1;
   double alpha = (auxf1 > auxf2) ? alpha1 : alpha2;
 
diff --git a/src/transform/fmllr-raw-test.cc b/src/transform/fmllr-raw-test.cc
index f51c77304ce..dc9d9beeb99 100644
--- a/src/transform/fmllr-raw-test.cc
+++ b/src/transform/fmllr-raw-test.cc
@@ -36,10 +36,10 @@ void InitRandomGmm (DiagGmm *gmm_in) {
   Vector<BaseFloat> weights(num_gauss);
   for (int32 i = 0; i < num_gauss; i++) {
     for (int32 j = 0; j < dim; j++) {
-      inv_vars(i, j) = exp(RandGauss() * (1.0 / (1 + j)));
+      inv_vars(i, j) = Exp(RandGauss() * (1.0 / (1 + j)));
       means(i, j) = RandGauss() * (1.0 / (1 + j));
     }
-    weights(i) = exp(RandGauss());
+    weights(i) = Exp(RandGauss());
   }
   weights.Scale(1.0 / weights.Sum());
   gmm.SetWeights(weights);
diff --git a/src/transform/mllt.cc b/src/transform/mllt.cc
index 9c547ed9ce4..fb430b04542 100644
--- a/src/transform/mllt.cc
+++ b/src/transform/mllt.cc
@@ -105,12 +105,12 @@ void MlltAccs::Update(double beta,
       // for hidden markov models, eq.  (22)),
       // row = G_i^{-1} cofactor sqrt(beta / cofactor^T G_i^{-1} cofactor). (1)
       // here, "row" and "cofactor" are considered as column vectors.
-      double objf_before = beta * log(std::abs(VecVec(row, cofactor)))
+      double objf_before = beta * Log(std::abs(VecVec(row, cofactor)))
           -0.5 * VecSpVec(row, G[i], row);
       // do eq. (1) above:
       row.AddSpVec(std::sqrt(beta / VecSpVec(cofactor, Ginv[i], cofactor)),
                    Ginv[i], cofactor, 0.0);
-      double objf_after = beta * log(std::abs(VecVec(row, cofactor)))
+      double objf_after = beta * Log(std::abs(VecVec(row, cofactor)))
           -0.5 * VecSpVec(row, G[i], row);
       if (objf_after < objf_before - fabs(objf_before)*0.00001)
         KALDI_ERR << "Objective decrease in MLLT update.";
diff --git a/src/transform/regression-tree-test.cc b/src/transform/regression-tree-test.cc
index 7e9b8d179ee..27e61c3454b 100644
--- a/src/transform/regression-tree-test.cc
+++ b/src/transform/regression-tree-test.cc
@@ -67,7 +67,7 @@ rand_diag_gmm(size_t num_comp, size_t dim, DiagGmm *gmm) {
     weights(m) = kaldi::RandUniform();
     for (size_t d= 0; d < dim; d++) {
       means(m, d) = kaldi::RandGauss();
-      vars(m, d) = exp(kaldi::RandGauss()) + 1e-5;
+      vars(m, d) = Exp(kaldi::RandGauss()) + 1e-5;
     }
     tot_weight += weights(m);
   }
diff --git a/src/transform/regtree-fmllr-diag-gmm-test.cc b/src/transform/regtree-fmllr-diag-gmm-test.cc
index e90ad4ea9b1..7f471d83966 100644
--- a/src/transform/regtree-fmllr-diag-gmm-test.cc
+++ b/src/transform/regtree-fmllr-diag-gmm-test.cc
@@ -98,7 +98,7 @@ generate_features(cova_type covariance_type,
       // random diagonal covariance for gaussian j
       Sj.SetZero();
       for (size_t d = 0; d < dim; d++) {
-        Sj(d, d) = 2*exp(RandGauss());
+        Sj(d, d) = 2*Exp(RandGauss());
       }
     }
     if (covariance_type == full) {
diff --git a/src/util/text-utils.cc b/src/util/text-utils.cc
index 2628c690081..da6d0e0cc52 100644
--- a/src/util/text-utils.cc
+++ b/src/util/text-utils.cc
@@ -104,8 +104,8 @@ bool IsToken(const std::string &token) {
   size_t l = token.length();
   if (l == 0) return false;
   for (size_t i = 0; i < l; i++) {
-    char c = token[i];
-    if ( (!isprint(c) || isspace(c)) && (isascii(c) || c == (char)255) ) return false;
+    unsigned char c = token[i];
+    if ( (!isprint(c) || isspace(c)) && (isascii(c) || c == (unsigned char)255) ) return false;
     // The "&& (isascii(c) || c == 255)" was added so that we won't reject non-ASCII
     // characters such as French characters with accents [except for 255 which is
     // "nbsp", a form of space].
@@ -159,11 +159,65 @@ bool IsLine(const std::string &line) {
   return true;
 }
 
+
+inline bool starts_with(const std::string &in, const std::string &prefix) {
+  return in.substr(0, prefix.size()) == prefix;
+}
+
+inline bool stricmp(const std::string &in, const std::string &prefix) {
+  int ret = _stricmp(in.c_str(), prefix.c_str());
+  return ret == 0;
+}
+
+inline bool is_nan_text(const std::string &in, const std::string &prefix) {
+  if (in.size() < prefix.size())
+    return false;
+
+  if (stricmp(in, prefix))
+    return true;
+
+  for (int i = 0; i < prefix.size(); ++i)
+    if (tolower(in[i]) != tolower(prefix[i]))
+      return false;
+
+  for (int i = prefix.size(); i < in.size(); ++i)
+    if (!isalpha(in[i]) && (in[i] != '_'))
+      return false;
+
+  return true;
+}
+
+template<typename T>
+bool convert_special_number(const std::string &str, T *out) {
+  if (stricmp(str, "infinity") || stricmp(str, "inf") || starts_with(str, "1.#INF")) {
+    *out = std::numeric_limits<T>::infinity();
+    return true;
+  } else if (stricmp(str, "-infinity") || stricmp(str, "-inf") || starts_with(str, "-1.#INF")) {
+    *out = -std::numeric_limits<T>::infinity();
+    return true;
+  } else if (is_nan_text(str, "nan") || starts_with(str, "1.#QNAN")) {
+    *out = std::numeric_limits<T>::quiet_NaN();
+    return true;
+  } else if (is_nan_text(str, "-nan") || starts_with(str, "-1.#QNAN")) {
+    *out = -std::numeric_limits<T>::quiet_NaN();
+    return true;
+  }
+  return false;
+}
+
 bool ConvertStringToReal(const std::string &str,
                          double *out) {
   const char *this_str = str.c_str();
   char *end = NULL;
   errno = 0;
+
+#if defined(_MSC_VER)
+  // TODO: check if the new MSVC already supports it
+  // depending on claims of the C++11 support, it should have
+  if (convert_special_number(str, out))
+    return true;
+#endif // defined(_MSC_VER)
+
   double d = KALDI_STRTOD(this_str, &end);
   if (end != this_str)
     while (isspace(*end)) end++;
@@ -178,6 +232,14 @@ bool ConvertStringToReal(const std::string &str,
   const char *this_str = str.c_str();
   char *end = NULL;
   errno = 0;
+
+#ifdef _MSC_VER
+  // TODO: check if the new MSVC already supports it
+  // depending on claims of the C++11 support, it should have
+  if (convert_special_number(str, out))
+    return true;
+#endif // _MSC_VER
+
   float f = KALDI_STRTOF(this_str, &end);
   if (end != this_str)
     while (isspace(*end)) end++;
diff --git a/windows/generate_solution.pl b/windows/generate_solution.pl
index 73ae624b725..1fb2289f9c9 100755
--- a/windows/generate_solution.pl
+++ b/windows/generate_solution.pl
@@ -21,12 +21,7 @@
 use Data::Dumper;
 use Getopt::Long;
 
-my $root = "$Bin/..";
-my $solutionDir = "$root/kaldiwin_vs12_auto";
-my $projDir = "$solutionDir/kaldiwin";
-my $solutionFileName = "kaldiwin_vs12.sln";
-my $srcDir = "$root/src";
-my $vsver="vs2013";
+my $vsver="vs2015";
 
 my %ENABLED = (CUDA => 0,
                OPENBLAS => 1,
@@ -55,7 +50,28 @@
 	die "Unknown vsver value: $vsver";
 }
 			 
-
+my $features_suffix;
+my @features_enabled;
+
+if ($ENABLED{OPENBLAS}) {
+  push @features_enabled, "OPENBLAS";
+}
+if ($ENABLED{MKL}) {
+  push @features_enabled, "MKL";
+}
+if ($ENABLED{CUDA}) {
+  push @features_enabled, "CUDA";
+}
+
+$features_suffix = join("_", @features_enabled);
+
+my $root = "$Bin/..";
+my $solutionDir = "$root/kaldiwin_${vsver}_${features_suffix}";
+my $projDir = "$solutionDir/kaldiwin";
+my $solutionFileName = "kaldiwin_${vsver}.sln";
+my $srcDir = "$root/src";
+
+
 # The following files are in the same dir (windows/) as the 
 # Perl script.
 my @propsFiles = (

From b0439a2ee5b351b153e2f227239e926dcc6afe8c Mon Sep 17 00:00:00 2001
From: Jan Trmal <yendovy.konference@seznam.cz>
Date: Wed, 22 Jul 2015 13:22:48 -0400
Subject: [PATCH 2/2] fix nonexistent _stricmp under linux

---
 src/util/text-utils.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/util/text-utils.cc b/src/util/text-utils.cc
index da6d0e0cc52..28b2a9fb2dd 100644
--- a/src/util/text-utils.cc
+++ b/src/util/text-utils.cc
@@ -165,7 +165,7 @@ inline bool starts_with(const std::string &in, const std::string &prefix) {
 }
 
 inline bool stricmp(const std::string &in, const std::string &prefix) {
-  int ret = _stricmp(in.c_str(), prefix.c_str());
+  int ret = KALDI_STRCASECMP(in.c_str(), prefix.c_str());
   return ret == 0;
 }