Attempt at tracking states of the arts and recent results (bibliography) on speech recognition.
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README.md add TDNN-F Lexicon Aug 6, 2018

README.md

wer_are_we

WER are we? An attempt at tracking states of the art(s) and recent results on speech recognition. Feel free to correct! (Inspired by Are we there yet?)

WER

LibriSpeech

(Possibly trained on more data than LibriSpeech.)

WER test-clean WER test-other Paper Published Notes
5.83% 12.69% Deep Speech 2: End-to-End Speech Recognition in English and Mandarin December 2015 Humans
3.19% 7.64% The CAPIO 2017 Conversational Speech Recognition System April 2018 TDNN + TDNN-LSTM + CNN-bLSTM + Dense TDNN-LSTM across two kinds of trees
3.80% 8.76% Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks Interspeech, Sept 2018 Kaldi recipe, 17-layer TDNN-F + iVectors
3.82% 12.76% Improved training of end-to-end attention models for speech recognition Interspeech, Sept 2018 encoder-attention-decoder end-to-end model
4.28% Purely sequence-trained neural networks for ASR based on lattice-free MMI September 2016 HMM-TDNN trained with MMI + data augmentation (speed) + iVectors + 3 regularizations
4.83% A time delay neural network architecture for efficient modeling of long temporal contexts 2015 HMM-TDNN + iVectors
5.33% 13.25% Deep Speech 2: End-to-End Speech Recognition in English and Mandarin December 2015 9-layer model w/ 2 layers of 2D-invariant convolution & 7 recurrent layers, w/ 100M parameters trained on 11940h
5.51% 13.97% LibriSpeech: an ASR Corpus Based on Public Domain Audio Books 2015 HMM-DNN + pNorm*
4.8% 14.5% Letter-Based Speech Recognition with Gated ConvNets December 2017 (Gated) ConvNet for AM going to letters + 4-gram LM
8.01% 22.49% same, Kaldi 2015 HMM-(SAT)GMM
12.51% Audio Augmentation for Speech Recognition 2015 TDNN + pNorm + speed up/down speech

WSJ

(Possibly trained on more data than WSJ.)

WER eval'92 WER eval'93 Paper Published Notes
3.47% Deep Recurrent Neural Networks for Acoustic Modelling April 2015 TC-DNN-BLSTM-DNN
5.03% 8.08% Deep Speech 2: End-to-End Speech Recognition in English and Mandarin December 2015 Humans
3.63% 5.66% LibriSpeech: an ASR Corpus Based on Public Domain Audio Books 2015 test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*
3.60% 4.98% Deep Speech 2: End-to-End Speech Recognition in English and Mandarin December 2015 9-layer model w/ 2 layers of 2D-invariant convolution & 7 recurrent layers, w/ 100M parameters
5.6% Convolutional Neural Networks-based Continuous Speech Recognition using Raw Speech Signal 2014 CNN over RAW speech (wav)

Hub5'00 Evaluation (Switchboard / CallHome)

(Possibly trained on more data than SWB, but test set = full Hub5'00.)

WER (SWB) WER (CH) Paper Published Notes
5.0% 9.1% The CAPIO 2017 Conversational Speech Recognition System December 2017 2 Dense LSTMs + 3 CNN-bLSTMs across 3 phonesets from previous Capio paper & AM adaptation using parameter averaging (5.6% SWB / 10.5% CH single systems)
5.1% 9.9% Language Modeling with Highway LSTM September 2017 HW-LSTM LM trained with Switchboard+Fisher+Gigaword+Broadcast News+Conversations, AM from previous IBM paper
5.1% The Microsoft 2017 Conversational Speech Recognition System August 2017 ~2016 system + character-based dialog session aware (turns of speech) LSTM LM
5.3% 10.1% Deep Learning-based Telephony Speech Recognition in the Wild August 2017 Ensemble of 3 CNN-bLSTM (5.7% SWB / 11.3% CH single systems)
5.5% 10.3% English Conversational Telephone Speech Recognition by Humans and Machines March 2017 ResNet + BiLSTMs acoustic model, with 40d FMLLR + i-Vector inputs, trained on SWB+Fisher+CH, n-gram + model-M + LSTM + Strided (à trous) convs-based LM trained on Switchboard+Fisher+Gigaword+Broadcast
6.3% 11.9% The Microsoft 2016 Conversational Speech Recognition System September 2016 VGG/Resnet/LACE/BiLSTM acoustic model trained on SWB+Fisher+CH, N-gram + RNNLM language model trained on Switchboard+Fisher+Gigaword+Broadcast
6.6% 12.2% The IBM 2016 English Conversational Telephone Speech Recognition System June 2016 RNN + VGG + LSTM acoustic model trained on SWB+Fisher+CH, N-gram + "model M" + NNLM language model
8.5% 13% Purely sequence-trained neural networks for ASR based on lattice-free MMI September 2016 HMM-BLSTM trained with MMI + data augmentation (speed) + iVectors + 3 regularizations + Fisher
9.2% 13.3% Purely sequence-trained neural networks for ASR based on lattice-free MMI September 2016 HMM-TDNN trained with MMI + data augmentation (speed) + iVectors + 3 regularizations + Fisher (10% / 15.1% respectively trained on SWBD only)
12.6% 16% Deep Speech: Scaling up end-to-end speech recognition December 2014 CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trained only on SWB
11% 17.1% A time delay neural network architecture for efficient modeling of long temporal contexts 2015 HMM-TDNN + iVectors
12.6% 18.4% Sequence-discriminative training of deep neural networks 2013 HMM-DNN +sMBR
12.9% 19.3% Audio Augmentation for Speech Recognition 2015 HMM-TDNN + pNorm + speed up/down speech
15% 19.1% Building DNN Acoustic Models for Large Vocabulary Speech Recognition June 2014 DNN + Dropout
10.4% Joint Training of Convolutional and Non-Convolutional Neural Networks 2014 CNN on MFSC/fbanks + 1 non-conv layer for FMLLR/I-Vectors concatenated in a DNN
11.5% Deep Convolutional Neural Networks for LVCSR 2013 CNN
12.2% Very Deep Multilingual Convolutional Neural Networks for LVCSR September 2015 Deep CNN (10 conv, 4 FC layers), multi-scale feature maps
11.8% 25.7% Improved training of end-to-end attention models for speech recognition Interspeech, Sept 2018 encoder-attention-decoder end-to-end model, trained on 300h SWB

Rich Transcriptions

WER RT-02 WER RT-03 WER RT-04 Paper Published Notes
8.1% 8.0% The CAPIO 2017 Conversational Speech Recognition System April 2018 2 Dense LSTMs + 3 CNN-bLSTMs across 3 phonesets from previous Capio paper & AM adaptation using parameter averaging
8.2% 8.1% 7.7% Language Modeling with Highway LSTM September 2017 HW-LSTM LM trained with Switchboard+Fisher+Gigaword+Broadcast News+Conversations, AM from previous IBM paper
8.3% 8.0% 7.7% English Conversational Telephone Speech Recognition by Humans and Machines March 2017 ResNet + BiLSTMs acoustic model, with 40d FMLLR + i-Vector inputs, trained on SWB+Fisher+CH, n-gram + model-M + LSTM + Strided (à trous) convs-based LM trained on Switchboard+Fisher+Gigaword+Broadcast

Fisher (RT03S FSH)

WER Paper Published Notes
9.6% Purely sequence-trained neural networks for ASR based on lattice-free MMI September 2016 HMM-BLSTM trained with MMI + data augmentation (speed) + iVectors + 3 regularizations + SWBD
9.8% Purely sequence-trained neural networks for ASR based on lattice-free MMI September 2016 HMM-TDNN trained with MMI + data augmentation (speed) + iVectors + 3 regularizations + SWBD

TED-LIUM

WER Test Paper Published Notes
6.5% The CAPIO 2017 Conversational Speech Recognition System April 2018 TDNN + TDNN-LSTM + CNN-bLSTM + Dense TDNN-LSTM across two kinds of trees
11.2% Purely sequence-trained neural networks for ASR based on lattice-free MMI September 2016 HMM-TDNN trained with LF-MMI + data augmentation (speed perturbation) + iVectors + 3 regularizations
15.3% TED-LIUM: an Automatic Speech Recognition dedicated corpus May 2014 Multi-layer perceptron (MLP) with bottle-neck feature extraction

CHiME (noisy speech)

clean real sim Paper Published Notes
3.34% 21.79% 45.05% Deep Speech 2: End-to-End Speech Recognition in English and Mandarin December 2015 9-layer model w/ 2 layers of 2D-invariant convolution & 7 recurrent layers, w/ 68M parameters
6.30% 67.94% 80.27% Deep Speech: Scaling up end-to-end speech recognition December, 2014 CNN + Bi-RNN + CTC (speech to letters)

TODO

PER

TIMIT

(So far, all results trained on TIMIT and tested on the standard test set.)

PER Paper Published Notes
16.5% Phone recognition with hierarchical convolutional deep maxout networks September 2015 Hierarchical maxout CNN + Dropout
16.5% A Regularization Post Layer: An Additional Way how to Make Deep Neural Networks Robust 2017 DBN with last layer regularization
16.7% Combining Time- and Frequency-Domain Convolution in Convolutional Neural Network-Based Phone Recognition 2014 CNN in time and frequency + dropout, 17.6% w/o dropout
17.3% Segmental Recurrent Neural Networks for End-to-end Speech Recognition March 2016 RNN-CRF on 24(x3) MFSC
17.6% Attention-Based Models for Speech Recognition June 2015 Bi-RNN + Attention
17.7% Speech Recognition with Deep Recurrent Neural Networks March 2013 Bi-LSTM + skip connections w/ RNN transducer (18.4% with CTC only)
18.0% Learning Filterbanks from Raw Speech for Phone Recognition October 2017 Complex ConvNets on raw speech w/ mel-fbanks init
18.8% Wavenet: A Generative Model For Raw Audio September 2016 Wavenet architecture with mean pooling layer after residual block + few non-causal conv layers
23% Deep Belief Networks for Phone Recognition 2009 (first, modern) HMM-DBN

LM

TODO

Noise-robust ASR

TODO

BigCorp™®-specific dataset

TODO?

Lexicon

  • WER: word error rate
  • PER: phone error rate
  • LM: language model
  • HMM: hidden markov model
  • GMM: Gaussian mixture model
  • DNN: deep neural network
  • CNN: convolutional neural network
  • DBN: deep belief network (RBM-based DNN)
  • TDNN-F: a factored form of time delay neural networks (TDNN)
  • RNN: recurrent neural network
  • LSTM: long short-term memory
  • CTC: connectionist temporal classification
  • MMI: maximum mutual information (MMI),
  • MPE: minimum phone error
  • sMBR: state-level minimum Bayes risk
  • SAT: speaker adaptive training
  • MLLR: maximum likelihood linear regression
  • LDA: (in this context) linear discriminant analysis
  • MFCC: Mel frequency cepstral coefficients
  • FB/FBANKS/MFSC: Mel frequency spectral coefficients
  • VGG: very deep convolutional neural networks from Visual Graphics Group, VGG is an architecture of 2 {3x3 convolutions} followed by 1 pooling, repeated