task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Speech Recognition | LibriSpeech test-clean | Zipformer+pruned transducer w/ CR-CTC (no external language model) | https://arxiv.org/abs/2410.05101v4 | Word Error Rate (WER) | 1.88 |
Speech Recognition | LibriSpeech test-clean | ContextNet(L) | https://arxiv.org/abs/2005.03191v3 | Word Error Rate (WER) | 1.9 |
Speech Recognition | LibriSpeech test-clean | Conformer(L) | https://arxiv.org/abs/2005.08100v1 | Word Error Rate (WER) | 1.9 |
Speech Recognition | LibriSpeech test-clean | Transformer+Time reduction+Self Knowledge distillation | https://arxiv.org/abs/2103.09903v1 | Word Error Rate (WER) | 1.9 |
Speech Recognition | LibriSpeech test-clean | ContextNet(M) | https://arxiv.org/abs/2005.03191v3 | Word Error Rate (WER) | 2 |
Speech Recognition | LibriSpeech test-clean | Transformer Transducer | https://arxiv.org/abs/2011.03109v2 | Word Error Rate (WER) | 2.0 |
Speech Recognition | LibriSpeech test-clean | Conformer(M) | https://arxiv.org/abs/2005.08100v1 | Word Error Rate (WER) | 2 |
Speech Recognition | LibriSpeech test-clean | SpeechStew (100M) | https://arxiv.org/abs/2104.02133v3 | Word Error Rate (WER) | 2.0 |
Speech Recognition | LibriSpeech test-clean | Qwen-Audio | https://arxiv.org/abs/2311.07919v2 | Word Error Rate (WER) | 2.0 |
Speech Recognition | LibriSpeech test-clean | Zipformer+pruned transducer (no external language model) | https://arxiv.org/abs/2310.11230v4 | Word Error Rate (WER) | 2.00 |
Speech Recognition | LibriSpeech test-clean | Zipformer+CR-CTC (no external language model) | https://arxiv.org/abs/2410.05101v4 | Word Error Rate (WER) | 2.02 |
Speech Recognition | LibriSpeech test-clean | Conv + Transformer AM + Pseudo-Labeling (ConvLM with Transformer Rescoring) | https://arxiv.org/abs/1911.08460v3 | Word Error Rate (WER) | 2.03 |
Speech Recognition | LibriSpeech test-clean | Conv + Transformer AM + Iterative Pseudo-Labeling (n-gram LM + Transformer Rescoring) | https://arxiv.org/abs/2005.09267v2 | Word Error Rate (WER) | 2.10 |
Speech Recognition | LibriSpeech test-clean | CTC + Transformer LM rescoring | https://arxiv.org/abs/2005.09150v2 | Word Error Rate (WER) | 2.10 |
Speech Recognition | LibriSpeech test-clean | Conformer(S) | https://arxiv.org/abs/2005.08100v1 | Word Error Rate (WER) | 2.1 |
Speech Recognition | LibriSpeech test-clean | Branchformer + GFSA | https://arxiv.org/abs/2312.04234v5 | Word Error Rate (WER) | 2.11 |
Speech Recognition | LibriSpeech test-clean | Multi-Stream Self-Attention With Dilated 1D Convolutions | https://arxiv.org/abs/1910.00716v1 | Word Error Rate (WER) | 2.20 |
Speech Recognition | LibriSpeech test-clean | LSTM Transducer | https://arxiv.org/abs/2104.03006v2 | Word Error Rate (WER) | 2.23 |
Speech Recognition | LibriSpeech test-clean | Hybrid + Transformer LM rescoring | https://arxiv.org/abs/1910.09799v2 | Word Error Rate (WER) | 2.26 |
Speech Recognition | LibriSpeech test-clean | Hybrid model with Transformer rescoring | https://arxiv.org/abs/1905.03072v3 | Word Error Rate (WER) | 2.3 |
Speech Recognition | LibriSpeech test-clean | ContextNet(S) | https://arxiv.org/abs/2005.03191v3 | Word Error Rate (WER) | 2.3 |
Speech Recognition | LibriSpeech test-clean | Conv + Transformer AM (ConvLM with Transformer Rescoring) (LS only) | https://arxiv.org/abs/1911.08460v3 | Word Error Rate (WER) | 2.31 |
Speech Recognition | LibriSpeech test-clean | Squeezeformer (L) | https://arxiv.org/abs/2206.00888v2 | Word Error Rate (WER) | 2.47 |
Speech Recognition | LibriSpeech test-clean | LAS + SpecAugment | https://arxiv.org/abs/1904.08779v3 | Word Error Rate (WER) | 2.5 |
Speech Recognition | LibriSpeech test-clean | Transformer | https://arxiv.org/abs/1909.06317v2 | Word Error Rate (WER) | 2.6 |
Speech Recognition | LibriSpeech test-clean | QuartzNet15x5 | https://arxiv.org/abs/1910.10261v1 | Word Error Rate (WER) | 2.69 |
Speech Recognition | LibriSpeech test-clean | LAS (no LM) | https://arxiv.org/abs/1904.08779v3 | Word Error Rate (WER) | 2.7 |
Speech Recognition | LibriSpeech test-clean | wav2vec_wav2letter | https://arxiv.org/abs/2010.11430v1 | Word Error Rate (WER) | 2.7 |
Speech Recognition | LibriSpeech test-clean | Espresso | https://arxiv.org/abs/1909.08723v3 | Word Error Rate (WER) | 2.8 |
Speech Recognition | LibriSpeech test-clean | Jasper DR 10x5 (+ Time/Freq Masks) | https://arxiv.org/abs/1904.03288v3 | Word Error Rate (WER) | 2.84 |
Speech Recognition | LibriSpeech test-clean | Jasper DR 10x5 | https://arxiv.org/abs/1904.03288v3 | Word Error Rate (WER) | 2.95 |
Speech Recognition | LibriSpeech test-clean | tdnn + chain + rnnlm rescoring | https://www.cs.jhu.edu/~hxu/neural-network-language.pdf | Word Error Rate (WER) | 3.06 |
Speech Recognition | LibriSpeech test-clean | Convolutional Speech Recognition | http://arxiv.org/abs/1812.06864v2 | Word Error Rate (WER) | 3.26 |
Speech Recognition | LibriSpeech test-clean | MT4SSL | https://arxiv.org/abs/2211.07321v3 | Word Error Rate (WER) | 3.4 |
Speech Recognition | LibriSpeech test-clean | Model Unit Exploration | https://arxiv.org/abs/1902.01955v2 | Word Error Rate (WER) | 3.60 |
Speech Recognition | LibriSpeech test-clean | Seq-to-seq attention | http://arxiv.org/abs/1805.03294v1 | Word Error Rate (WER) | 3.82 |
Speech Recognition | LibriSpeech test-clean | CTC-CRF 4gram-LM | https://ieeexplore.ieee.org/document/8682256 | Word Error Rate (WER) | 4.09 |
Speech Recognition | LibriSpeech test-clean | HMM-TDNN trained with MMI + data augmentation (speed) + iVectors + 3 regularizations | null | Word Error Rate (WER) | 4.3 |
Speech Recognition | LibriSpeech test-clean | Centaurus (30 M) | https://arxiv.org/abs/2501.13230v1 | Word Error Rate (WER) | 4.4 |
Speech Recognition | LibriSpeech test-clean | HMM-TDNN + iVectors | null | Word Error Rate (WER) | 4.8 |
Speech Recognition | LibriSpeech test-clean | Gated ConvNets | http://arxiv.org/abs/1712.09444v2 | Word Error Rate (WER) | 4.8 |
Speech Recognition | LibriSpeech test-clean | Deep Speech 2 | http://arxiv.org/abs/1512.02595v1 | Word Error Rate (WER) | 5.33 |
Speech Recognition | LibriSpeech test-clean | CTC + policy learning | http://arxiv.org/abs/1712.07101v1 | Word Error Rate (WER) | 5.42 |
Speech Recognition | LibriSpeech test-clean | HMM-DNN + pNorm* | null | Word Error Rate (WER) | 5.5 |
Speech Recognition | LibriSpeech test-clean | Li-GRU | http://arxiv.org/abs/1811.07453v2 | Word Error Rate (WER) | 6.2 |
Speech Recognition | LibriSpeech test-clean | Snips | http://arxiv.org/abs/1805.10190v3 | Word Error Rate (WER) | 6.4 |
Speech Recognition | LibriSpeech test-clean | Local Prior Matching (Large Model) | https://arxiv.org/abs/2002.10336v1 | Word Error Rate (WER) | 7.19 |
Speech Recognition | LibriSpeech test-clean | HMM-(SAT)GMM | null | Word Error Rate (WER) | 8.0 |
Speech Recognition | LibriSpeech test-clean | AmNet | https://arxiv.org/abs/2108.01553v1 | Word Error Rate (WER) | 8.6 |
Speech Recognition | GigaSpeech TEST | Zipformer+pruned transducer w/ CR-CTC
(no external language model) | https://arxiv.org/abs/2410.05101v4 | Word Error Rate (WER) | 10.03 |
Speech Recognition | GigaSpeech TEST | Zipformer+CR-CTC/AED
(no external language model) | https://arxiv.org/abs/2410.05101v4 | Word Error Rate (WER) | 10.07 |
Speech Recognition | GigaSpeech TEST | Zipformer+pruned transducer
(no external language model) | https://arxiv.org/abs/2410.05101v4 | Word Error Rate (WER) | 10.2 |
Speech Recognition | GigaSpeech TEST | Zipformer+CR-CTC
(no external language model) | https://arxiv.org/abs/2410.05101v4 | Word Error Rate (WER) | 10.28 |
Speech Recognition | GigaSpeech TEST | Conformer/Transformer-AED | https://arxiv.org/abs/2106.06909v1 | Word Error Rate (WER) | 10.80 |
Speech Recognition | VietMed | XLSR-53-Viet | https://arxiv.org/abs/2404.05659v3 | Dev WER | 26.8 |
Speech Recognition | VietMed | XLSR-53-Viet | https://arxiv.org/abs/2404.05659v3 | Test WER | 29.6 |
Speech Recognition | VietMed | XLSR-53 | https://arxiv.org/abs/2404.05659v3 | Dev WER | 45.2 |
Speech Recognition | VietMed | XLSR-53 | https://arxiv.org/abs/2404.05659v3 | Test WER | 51.8 |
Speech Recognition | VietMed | w2v2-Viet | https://arxiv.org/abs/2404.05659v3 | Dev WER | 45.3 |
Speech Recognition | VietMed | w2v2-Viet | https://arxiv.org/abs/2404.05659v3 | Test WER | 49.5 |
Speech Recognition | VietMed | GMM-HMM SAT+VTLN | https://arxiv.org/abs/2404.05659v3 | Dev WER | 52.2 |
Speech Recognition | VietMed | GMM-HMM SAT | https://arxiv.org/abs/2404.05659v3 | Dev WER | 52.6 |
Speech Recognition | VietMed | GMM-HMM Tri | https://arxiv.org/abs/2404.05659v3 | Dev WER | 61.3 |
Speech Recognition | VietMed | GMM-HMM VTLN | https://arxiv.org/abs/2404.05659v3 | Dev WER | 61.3 |
Speech Recognition | VietMed | GMM-HMM Mono | https://arxiv.org/abs/2404.05659v3 | Dev WER | 71.7 |
Speech Recognition | Europarl-ASR EN Guest-test | United-MedASR (764M) | https://arxiv.org/abs/2412.00055v1 | WER | 0.26 |
Speech Recognition | Europarl-ASR EN Guest-test | mllp_2021_offline_verb | https://www.isca-speech.org/archive/interspeech_2021/diazmunio21_interspeech.html | WER | 7.0 |
Speech Recognition | Europarl-ASR EN Guest-test | mllp_2021_streaming_verb | https://www.isca-speech.org/archive/interspeech_2021/diazmunio21_interspeech.html | WER | 7.3 |
Speech Recognition | Switchboard + Hub500 | IBM (LSTM+Conformer encoder-decoder) | https://arxiv.org/abs/2105.00982v1 | Percentage error | 4.3 |
Speech Recognition | Switchboard + Hub500 | IBM (LSTM encoder-decoder) | https://arxiv.org/abs/2001.07263v3 | Percentage error | 4.7 |
Speech Recognition | Switchboard + Hub500 | ResNet + BiLSTMs acoustic model | http://arxiv.org/abs/1703.02136v1 | Percentage error | 5.5 |
Speech Recognition | Switchboard + Hub500 | Microsoft 2016b | http://arxiv.org/abs/1610.05256v2 | Percentage error | 5.8 |
Speech Recognition | Switchboard + Hub500 | Microsoft 2016 | http://arxiv.org/abs/1609.03528v2 | Percentage error | 6.2 |
Speech Recognition | Switchboard + Hub500 | VGG/Resnet/LACE/BiLSTM acoustic model trained on SWB+Fisher+CH, N-gram + RNNLM language model trained on Switchboard+Fisher+Gigaword+Broadcast | http://arxiv.org/abs/1609.03528v2 | Percentage error | 6.3 |
Speech Recognition | Switchboard + Hub500 | RNN + VGG + LSTM acoustic model trained on SWB+Fisher+CH, N-gram + "model M" + NNLM language model | http://arxiv.org/abs/1604.08242v2 | Percentage error | 6.6 |
Speech Recognition | Switchboard + Hub500 | CNN-LSTM | http://arxiv.org/abs/1610.05256v2 | Percentage error | 6.6 |
Speech Recognition | Switchboard + Hub500 | IBM 2016 | http://arxiv.org/abs/1604.08242v2 | Percentage error | 6.9 |
Speech Recognition | Switchboard + Hub500 | RNNLM | http://arxiv.org/abs/1609.03528v2 | Percentage error | 6.9 |
Speech Recognition | Switchboard + Hub500 | IBM 2015 | http://arxiv.org/abs/1505.05899v1 | Percentage error | 8.0 |
Speech Recognition | Switchboard + Hub500 | HMM-BLSTM trained with MMI + data augmentation (speed) + iVectors + 3 regularizations + Fisher | null | Percentage error | 8.5 |
Speech Recognition | Switchboard + Hub500 | HMM-TDNN trained with MMI + data augmentation (speed) + iVectors + 3 regularizations + Fisher (10% / 15.1% respectively trained on SWBD only) | null | Percentage error | 9.2 |
Speech Recognition | Switchboard + Hub500 | CNN on MFSC/fbanks + 1 non-conv layer for FMLLR/I-Vectors concatenated in a DNN | null | Percentage error | 10.4 |
Speech Recognition | Switchboard + Hub500 | HMM-TDNN + iVectors | null | Percentage error | 11 |
Speech Recognition | Switchboard + Hub500 | CNN | null | Percentage error | 11.5 |
Speech Recognition | Switchboard + Hub500 | Deep CNN (10 conv, 4 FC layers), multi-scale feature maps | http://arxiv.org/abs/1509.08967v2 | Percentage error | 12.2 |
Speech Recognition | Switchboard + Hub500 | HMM-DNN +sMBR | null | Percentage error | 12.6 |
Speech Recognition | Switchboard + Hub500 | DNN sMBR | null | Percentage error | 12.6 |
Speech Recognition | Switchboard + Hub500 | Deep Speech + FSH | http://arxiv.org/abs/1412.5567v2 | Percentage error | 12.6 |
Speech Recognition | Switchboard + Hub500 | CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWB | http://arxiv.org/abs/1412.5567v2 | Percentage error | 12.6 |
Speech Recognition | Switchboard + Hub500 | DNN MMI | null | Percentage error | 12.9 |
Speech Recognition | Switchboard + Hub500 | DNN MPE | null | Percentage error | 12.9 |
Speech Recognition | Switchboard + Hub500 | DNN BMMI | null | Percentage error | 12.9 |
Speech Recognition | Switchboard + Hub500 | HMM-TDNN + pNorm + speed up/down speech | null | Percentage error | 12.9 |
Speech Recognition | Switchboard + Hub500 | DNN + Dropout | http://arxiv.org/abs/1406.7806v2 | Percentage error | 15 |
Speech Recognition | Switchboard + Hub500 | DNN | http://arxiv.org/abs/1406.7806v2 | Percentage error | 16 |
Speech Recognition | Switchboard + Hub500 | CD-DNN | null | Percentage error | 16.1 |
Speech Recognition | Switchboard + Hub500 | DNN-HMM | null | Percentage error | 18.5 |
Speech Recognition | Switchboard + Hub500 | Deep Speech | http://arxiv.org/abs/1412.5567v2 | Percentage error | 20 |
Speech Recognition | LibriSpeech train-clean-100 test-clean | wav2vec_wav2letter | https://arxiv.org/abs/2010.11430v1 | Word Error Rate (WER) | 2.8 |
Speech Recognition | LibriSpeech test-other | SAMBA ASR | https://arxiv.org/abs/2501.02832v3 | Word Error Rate (WER) | 2.48 |
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