Update README.txt in reverb

egs/reverb/s5/README.txt

@@ -1,130 +1,36 @@
-Improved multi condition training baseline for REVERB challenge based on Kaldi
-==============================================================================
+Improved baseline for REVERB challenge 
+======================================
 
-updated
-Wed Apr 29 19:10:33 EDT 2015 Shinji Watanabe <watanabe@merl.com>
-
-updated 
-Wed Apr  9 12:14:02 CEST 2014 Felix Weninger <felix@weninger.de>
-
-original:
-Wed Nov  6 14:47:59 EST 2013 Felix Weninger <felix@weninger.de>
+This is an improvement over "Improved multi condition training baseline" from Felix Weninger & Shinji Watanabe
 
 Key specs:
-- MFCC-LDA-STC front-end
-- Boosted MMI trained GMM-HMM
-- Utterance-based adaptation using basis fMLLR
-- Tri-gram LM minimum Bayes risk decoding
-
-WER [%]
-@ Language model weight = 15
-Avg(SimData_(far|near)) = 11.73
-Avg(RealData)           = 30.44
-@ Language model weight = 16 (optimal)
-Avg(SimData_(far|near)) = 11.72
-Avg(RealData)           = 30.28
-
-See RESULTS in more detail
-
-Kaldi SVN rev. 5035, 4/26/15
-tested on Ubuntu 13.04
+- Nara-WPE and BeamformIt front-end enhancement
+- TDNN acoustic model
 
+RESULT:
+For experiment results, please see RESULTS for more detail
 
 REFERENCE:
 ++++++++
 If you find this software useful for your own research, please cite the
-following paper:
+following papers:
 
 Felix Weninger, Shinji Watanabe, Jonathan Le Roux, John R. Hershey, Yuuki
 Tachioka, Jürgen Geiger, Björn Schuller, Gerhard Rigoll: "The MERL/MELCO/TUM
 system for the REVERB Challenge using Deep Recurrent Neural Network Feature
 Enhancement", Proc. REVERB Workshop, IEEE, Florence, Italy, May 2014.
 
+Lukas Drude, Jahn Heymann, Christoph Boeddeker, and Reinhold Haeb-Umbach:
+"NARA-WPE: A Python package for weighted prediction error dereverberation in
+Numpy and Tensorflow for online and offline processing." In Speech Communication;
+13th ITG-Symposium, pp. 1-5. VDE, 2018.
 
 INSTRUCTIONS:
 +++++++++++++
-
-1) Set the path names in corpus.sh.default, 
-   and copy this file to "corpus.sh"
-
------
-2) [optional:] If you have speech enhancement (processed waveforms), then
-
-3a) Change directories and data preparation steps
-    For example, you could have something like
-
-    local/REVERB_wsjcam0_data_prep.sh /path/to/processed/REVERB_WSJCAM0_dt REVERB_dt_derev dt
-
-    The first argument is supposed to point to a folder that has the same
-    structure as the REVERB corpus.
-
-3b) run the multi-condition training steps in run.sh with the processed
-    training set, e.g., REVERB_tr_cut_derev, if you want to investigate
-    recognizer re-training
-
-    - Any system that has _mc in its name uses multi-condition training
-    - You probably want to change the system names if you are using enhanced
-      data for training (e.g. tri2b_mc -> tri2b_mc_derev)
-
-3c) Add your re-trained recognizer to the list of recognizers that are
-    discriminatively re-trained
-
-3d) Modify the decoding steps in run.sh so that they use enhanced data and add
-    your re-trained recognizer(s) to the list
------
-
-4) Execute the training and recognition steps by
+1) Execute the training and recognition steps by
 
    ./run.sh
 
    Depending on your system specs (# of CPUs, RAM) you might want (or have) to 
-   change the number of parallel jobs -- this is controlled by the nj_train,
-   nj_bg, and nj_tg variables (# of jobs for training, for bi-gram and tri-gram
-   decoding).
-
-   If you also want to have the re-implementation of the HTK baseline in Kaldi 
-   (tri2a and tri2a_mc systems), set the do_tri2a variable to true in run.sh.
-
-5) Execute 
-
-   ./local/get_results.sh 
-
-   to display the results corresponding to Table 1 in
-   the following paper,
-
-   Felix Weninger, Shinji Watanabe, Jonathan Le Roux, John R. Hershey, Yuuki
-   Tachioka, Jürgen Geiger, Björn Schuller, Gerhard Rigoll: "The MERL/MELCO/TUM
-   system for the REVERB Challenge using Deep Recurrent Neural Network Feature
-   Enhancement", to appear in Proc. REVERB Workshop, IEEE, Florence, Italy, 2014.
-
-   NOTE: It is very common to have slightly different results (up to +/- 1%
-   absolute WER per REVERB task file) on different machines.  The reason for
-   this is not fully known.
-
-   NOTE 2: By default, only the LDA-STC systems are trained - set do_tri2a in
-   run.sh to true to also train the Delta+Delta-Delta systems (cf. above).
-
------
-6) You can get more recognition results (for other combinations of front-ends, 
-   adaptation, language model, etc.), by 
-
-   $> local/summarize_results.pl [options] <system_name> [ <decoding_prefix> [ <data_suffix ] ]
-
-   where system_name is, e.g., tri2b_mc, or tri2b_mc_derev 
-   (a hypothetical system trained on dereverberated data)
-
-   decoding_prefix: one of basis_fmllr, mbr, mbr_basis_fmllr, or '' (empty)
-    - if the string "basis_fmllr" is given, (basis) fMLLR results are displayed
-    - if mbr is given, minimum Bayes risk decoding results are displayed
-    - if '' is given, no adaptation is used and ML decoding is used
-
-   data_suffix is, e.g., "derev" if your data sets are named "REVERB_dt_derev", etc.
-
-   By default, the optimum language model weight across all conditions is selected and
-   displayed. Note that Table 1 in the above paper uses a constant weight of 15.
-
-   Options: 
-   --lmw=x      Set fixed language model weight instead of best, x \in { 9, ..., 20 }
-   --lm=xg_5k   Display tri-gram (x=t) or bi-gram (x=b) LM decoding results
-   ----
-
+   change the number of parallel jobs -- this is controlled by the nj
+   and decode_nj variables (# of jobs for training, for decoding).