Speech Recognition on Hub5'00 SWB (test)
6.7WERResNet+LSTM1+LSTM2
Evaluation Results
| Method | Links | |
|---|---|---|
| ResNet+LSTM1+LSTM2Type=Frame-level score fusion2017.03 | 6.7 | |
| ResNet+LSTM2Type=Frame-level score fusion2017.03 | 6.8 | |
| LAS + SpecAugment (SM)Architecture=LAS, Policy=SM, Language Model=Yes2019.04 | 6.8 | |
| LAS + SpecAugment (SS)Architecture=LAS, Policy=SS, Language Model=Yes2019.04 | 7.1 | |
| LSTM2 (Feat. fusion)Type=Individual model2017.03 | 7.2 | |
| LAS + SpecAugment (SM)Architecture=LAS, Policy=SM, Language Model=No2019.04 | 7.2 | |
| LAS + SpecAugment (SS)Architecture=LAS, Policy=SS, Language Model=No2019.04 | 7.3 | |
| LSTM1 (SA-MTL)Type=Individual model2017.03 | 7.6 | |
| ResNetType=Individual model2017.03 | 7.6 | |
| This systemAM training data=SWB+Fisher+CH2015.05 | 8 | |
| hybridLM=LSTM, label unit=CDp2018.05 | 8.3 | |
| Zeyer et al. [24]Architecture=HMM, Language Model=Yes2019.04 | 8.3 | |
| Hadian et al.Architecture=HMM, Language Model=Yes2019.04 | 9.3 | |
| LF MMILM=4-gram, label unit=CDp2018.05 | 9.6 | |
| Povey et al.Architecture=HMM, Language Model=Yes2019.04 | 9.6 | |
| hybridLM=4-gram, label unit=CDp2018.05 | 9.8 | |
| MLP/CNN+I-Vector2014.12 | 10.4 | |
| Soltau et al.AM training data=SWB2015.05 | 10.4 | |
| LAS (Ours)Architecture=LAS, Language Model=Yes2019.04 | 10.9 | |
| Zeyer et al. [38]Architecture=LAS, Language Model=Yes2019.04 | 11 | |
| LAS (Ours)Architecture=LAS, Language Model=No2019.04 | 11.2 | |
| CNN-HMM2014.12 | 11.5 | |
| WDXNumber of convolutional layers=10, Optimization strategy=SGD from random initialization, # params (M)=41.3, #M frames=3202015.09 | 11.8 | |
| attention modelLM=LSTM, label unit=BPE 1K2018.05 | 11.8 | |
| Zeyer et al. [24]Architecture=LAS, Language Model=Yes2019.04 | 11.8 | |
| VDXNumber of convolutional layers=8, Optimization strategy=SGD from random initialization, # params (M)=38.4, #M frames=3402015.09 | 11.9 | |
| Zeyer et al. [38]Architecture=LAS, Language Model=No2019.04 | 11.9 | |
| WDXNumber of convolutional layers=10, Optimization strategy=Adadelta + SGD finetuning, # params (M)=41.3, #M frames=1402015.09 | 12.2 | |
| Weng et al.Architecture=LAS, Language Model=No2019.04 | 12.2 | |
| VDXNumber of convolutional layers=8, Optimization strategy=Adadelta + SGD finetuning, # params (M)=38.4, #M frames=1702015.09 | 12.3 | |
| DNN-HMM sMBR2014.12 | 12.6 | |
| Deep SpeechTraining Data=SWB + FSH2014.12 | 12.6 | |
| Vesely et al.AM training data=SWB2015.05 | 12.6 | |
| Hannun et al.AM training data=SWB+Fisher2015.05 | 12.6 | |
| Veselý et al.Architecture=HMM, Language Model=Yes2019.04 | 12.9 | |
| Seide et al.AM training data=SWB+Fisher+other2015.05 | 13.1 | |
| VCXNumber of convolutional layers=6, Optimization strategy=Adadelta + SGD finetuning, # params (M)=36.9, #M frames=2902015.09 | 13.1 | |
| attention modelLM=none, label unit=BPE 1K2018.05 | 13.1 | |
| Zeyer et al. [24]Architecture=LAS, Language Model=No2019.04 | 13.1 | |
| Classic 512# params (M)=41.2, #M frames=12002015.09 | 13.2 | |
| DNN-HMM sMBR HF2014.12 | 13.3 | |
| attention modelLM=none, label unit=BPE 10K2018.05 | 13.5 | |
| Classic 256 ReLUOptimization strategy=Adadelta + SGD finetuning, # params (M)=58.7, #M frames=2902015.09 | 13.8 | |
| CTCLM=word RNN, label unit=chars, added lexicon=true2018.05 | 14 | |
| Zweig et al.Architecture=CTC, Language Model=Yes2019.04 | 14 | |
| LAS + SpecAugment (SS)Architecture=LAS, Policy=SS, Language Model=Yes2019.04 | 14 | |
| LAS + SpecAugment (SM)Architecture=LAS, Policy=SM, Language Model=Yes2019.04 | 14.1 | |
| Zhou et al.AM training data=SWB2015.05 | 14.2 | |
| Maas et al.AM training data=SWB2015.05 | 14.3 | |
| LAS + SpecAugment (SS)Architecture=LAS, Policy=SS, Language Model=No2019.04 | 14.4 | |
| DNN-HMMTraining Data=SWB2014.12 | 14.6 | |
| Audhkhasi et al. [45]Architecture=CTC, Language Model=No2019.04 | 14.6 | |
| LAS + SpecAugment (SM)Architecture=LAS, Policy=SM, Language Model=No2019.04 | 14.6 | |
| Maas et al.AM training data=SWB+Fisher2015.05 | 15 | |
| DNN-HMMTraining Data=FSH2014.12 | 16 | |
| CD-DNN2014.12 | 16.1 | |
| Zeyer et al. [24]Architecture=HMM, Language Model=Yes2019.04 | 17.3 | |
| GMM-HMM BMMI2014.12 | 18.6 | |
| Hadian et al.Architecture=HMM, Language Model=Yes2019.04 | 18.9 | |
| Povey et al.Architecture=HMM, Language Model=Yes2019.04 | 19.3 | |
| LAS (Ours)Architecture=LAS, Language Model=Yes2019.04 | 19.4 | |
| CTCLM=n-gram, label unit=chars2018.05 | 19.8 | |
| Deep SpeechTraining Data=SWB2014.12 | 20 | |
| CTCLM=RNN, label unit=chars, added noise=true2018.05 | 20 | |
| Audhkhasi et al. [44]Architecture=CTC, Language Model=No2019.04 | 20.8 | |
| CTCLM=RNN, label unit=chars2018.05 | 21.4 | |
| LAS (Ours)Architecture=LAS, Language Model=No2019.04 | 21.6 | |
| attentionLM=none, label unit=chars2018.05 | 23.1 | |
| Toshniwal et al.Architecture=LAS, Language Model=No2019.04 | 23.1 | |
| Zeyer et al. [38]Architecture=LAS, Language Model=Yes2019.04 | 23.1 | |
| Weng et al.Architecture=LAS, Language Model=No2019.04 | 23.3 | |
| Audhkhasi et al. [45]Architecture=CTC, Language Model=No2019.04 | 23.6 | |
| Zeyer et al. [38]Architecture=LAS, Language Model=No2019.04 | 23.7 | |
| Veselý et al.Architecture=HMM, Language Model=Yes2019.04 | 24.5 | |
| CTCLM=none, label unit=chars2018.05 | 24.7 | |
| Zweig et al.Architecture=CTC, Language Model=No2019.04 | 24.7 | |
| Zweig et al.Architecture=CTC, Language Model=Yes2019.04 | 25.3 | |
| Zeyer et al. [24]Architecture=LAS, Language Model=Yes2019.04 | 25.7 | |
| attentionLM=3-gram, label unit=words2018.05 | 25.8 | |
| Lu et al.Architecture=LAS, Language Model=Yes2019.04 | 25.8 | |
| Zeyer et al. [24]Architecture=LAS, Language Model=No2019.04 | 26.1 | |
| attentionLM=none, label unit=words2018.05 | 26.8 | |
| Lu et al.Architecture=LAS, Language Model=No2019.04 | 26.8 | |
| Audhkhasi et al. [44]Architecture=CTC, Language Model=No2019.04 | 30.4 | |
| attentionLM=5-gram, label unit=chars2018.05 | 30.5 | |
| attentionLM=none, label unit=chars2018.05 | 32.8 | |
| Zweig et al.Architecture=CTC, Language Model=No2019.04 | 37.1 | |
| CTCLM=none, label unit=chars2018.05 | 38 | |
| Toshniwal et al.Architecture=LAS, Language Model=No2019.04 | 40.8 | |
| Lu et al.Architecture=LAS, Language Model=Yes2019.04 | 46 | |
| Lu et al.Architecture=LAS, Language Model=No2019.04 | 48.2 |