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 | TUDA | Conformer-Transducer (no LM) | https://ieeexplore.ieee.org/abstract/document/9854978/ | Test WER | 5.82% |
Speech Recognition | TUDA | TDNN-HMM hybrid, FST (with RNNLM rescoring) | https://aclanthology.org/2022.konvens-1.11 | Test WER | 6.93% |
Speech Recognition | TUDA | QuartzNet15x5DE (D37) | https://arxiv.org/abs/2110.07982v1 | Test WER | 10.2% |
Speech Recognition | TUDA | IMS-Speech | https://arxiv.org/abs/1908.04743v1 | Test WER | 12.0% |
Speech Recognition | TUDA | Hybrid CTC/Attention | https://arxiv.org/abs/2007.09127v1 | Test WER | 12.8% |
Speech Recognition | TUDA | Kaldi | http://arxiv.org/abs/1807.10311v1 | Test WER | 14.4% |
Speech Recognition | TUDA | DeepSpeech-Polyglot | null | Test WER | 18.6% |
Speech Recognition | TUDA | Kaldi | https://link.springer.com/chapter/10.1007/978-3-319-24033-6_54 | Test WER | 20.5% |
Speech Recognition | TUDA | PocketSphinx | https://link.springer.com/chapter/10.1007/978-3-319-24033-6_54 | Test WER | 39.6% |
Speech Recognition | Fongbe audio | Triphone (39 features) + LDA and MLLT + SGMM | https://hal.archives-ouvertes.fr/hal-01436788 | Word Error Rate (WER) | 16.57 |
Speech Recognition | Fongbe audio | Triphone (39 features) + LDA and MLLT + SAT and FMLLR | https://hal.archives-ouvertes.fr/hal-01436788 | Word Error Rate (WER) | 17.77 |
Speech Recognition | Fongbe audio | Triphone (13 MFCC + delta + delta2) | https://hal.archives-ouvertes.fr/hal-01436788 | Word Error Rate (WER) | 26.75 |
Speech Recognition | LibriCSS | TS-SEP | https://arxiv.org/abs/2303.03849v3 | Word Error Rate (WER) | 3.27 |
Speech Recognition | LibriCSS | GSS + Transducer | https://arxiv.org/abs/2212.05271v2 | Word Error Rate (WER) | 3.30 |
Speech Recognition | CHiME-6 dev_gss12 | ConformerXXL-PS + G-Augment | https://arxiv.org/abs/2210.10879v2 | Word Error Rate (WER) | 26 |
Speech Recognition | CHiME-6 dev_gss12 | ConformerXXL-PS | https://arxiv.org/abs/2109.13226v3 | Word Error Rate (WER) | 26.2 |
Speech Recognition | CHiME-6 dev_gss12 | SpeechStew (1B) | https://arxiv.org/abs/2104.02133v3 | Word Error Rate (WER) | 31.9 |
Speech Recognition | CHiME-6 dev_gss12 | RNN-T | https://arxiv.org/abs/2004.10799v3 | Word Error Rate (WER) | 55 |
Speech Recognition | swb_hub_500 WER fullSWBCH | IBM (LSTM+Conformer encoder-decoder) | https://arxiv.org/abs/2105.00982v1 | Percentage error | 6.8 |
Speech Recognition | swb_hub_500 WER fullSWBCH | IBM (LSTM encoder-decoder) | https://arxiv.org/abs/2001.07263v3 | Percentage error | 7.8 |
Speech Recognition | swb_hub_500 WER fullSWBCH | ResNet + BiLSTMs acoustic model | http://arxiv.org/abs/1703.02136v1 | Percentage error | 10.3 |
Speech Recognition | swb_hub_500 WER fullSWBCH | 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 | 11.9 |
Speech Recognition | swb_hub_500 WER fullSWBCH | 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 | 12.2 |
Speech Recognition | swb_hub_500 WER fullSWBCH | HMM-BLSTM trained with MMI + data augmentation (speed) + iVectors + 3 regularizations + Fisher | null | Percentage error | 13 |
Speech Recognition | swb_hub_500 WER fullSWBCH | HMM-TDNN trained with MMI + data augmentation (speed) + iVectors + 3 regularizations + Fisher (10% / 15.1% respectively trained on SWBD only) | null | Percentage error | 13.3 |
Speech Recognition | swb_hub_500 WER fullSWBCH | CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWB | http://arxiv.org/abs/1412.5567v2 | Percentage error | 16 |
Speech Recognition | swb_hub_500 WER fullSWBCH | HMM-TDNN + iVectors | null | Percentage error | 17.1 |
Speech Recognition | swb_hub_500 WER fullSWBCH | HMM-DNN +sMBR | null | Percentage error | 18.4 |
Speech Recognition | swb_hub_500 WER fullSWBCH | DNN + Dropout | http://arxiv.org/abs/1406.7806v2 | Percentage error | 19.1 |
Speech Recognition | swb_hub_500 WER fullSWBCH | HMM-TDNN + pNorm + speed up/down speech | null | Percentage error | 19.3 |
Speech Recognition | Europarl-ASR EN MEP-test | mllp_2021_offline_filt | https://www.isca-speech.org/archive/interspeech_2021/diazmunio21_interspeech.html | WER | 7.8 |
Speech Recognition | Europarl-ASR EN MEP-test | mllp_2021_streaming_filt | https://www.isca-speech.org/archive/interspeech_2021/diazmunio21_interspeech.html | WER | 7.9 |
Speech Recognition | AISHELL-2 Test Mic | Qwen-Audio | https://arxiv.org/abs/2311.07919v2 | Word Error Rate (WER) | 3.3 |
Speech Recognition | Common Voice Japanese | Whisper (Large v2) | https://arxiv.org/abs/2212.04356v1 | Test WER | 9.1% |
Speech Recognition | MediaSpeech | Quartznet | https://arxiv.org/abs/2103.16193v1 | WER for Arabic | 0.1300 |
Speech Recognition | MediaSpeech | Quartznet | https://arxiv.org/abs/2103.16193v1 | WER for French | 0.1915 |
Speech Recognition | MediaSpeech | Quartznet | https://arxiv.org/abs/2103.16193v1 | WER for Turkish | 0.1422 |
Speech Recognition | MediaSpeech | Quartznet | https://arxiv.org/abs/2103.16193v1 | WER for Spanish | 0.1826 |
Speech Recognition | MediaSpeech | Wit | https://arxiv.org/abs/2103.16193v1 | WER for Arabic | 0.2333 |
Speech Recognition | MediaSpeech | Wit | https://arxiv.org/abs/2103.16193v1 | WER for French | 0.1759 |
Speech Recognition | MediaSpeech | Wit | https://arxiv.org/abs/2103.16193v1 | WER for Turkish | 0.0768 |
Speech Recognition | MediaSpeech | Wit | https://arxiv.org/abs/2103.16193v1 | WER for Spanish | 0.0879 |
Speech Recognition | MediaSpeech | Azure | https://arxiv.org/abs/2103.16193v1 | WER for Arabic | 0.3016 |
Speech Recognition | MediaSpeech | Azure | https://arxiv.org/abs/2103.16193v1 | WER for French | 0.1683 |
Speech Recognition | MediaSpeech | Azure | https://arxiv.org/abs/2103.16193v1 | WER for Turkish | 0.2296 |
Speech Recognition | MediaSpeech | Azure | https://arxiv.org/abs/2103.16193v1 | WER for Spanish | 0.1296 |
Speech Recognition | MediaSpeech | VOSK | https://arxiv.org/abs/2103.16193v1 | WER for Arabic | 0.3085 |
Speech Recognition | MediaSpeech | VOSK | https://arxiv.org/abs/2103.16193v1 | WER for French | 0.2111 |
Speech Recognition | MediaSpeech | VOSK | https://arxiv.org/abs/2103.16193v1 | WER for Turkish | 0.3050 |
Speech Recognition | MediaSpeech | VOSK | https://arxiv.org/abs/2103.16193v1 | WER for Spanish | 0.1970 |
Speech Recognition | MediaSpeech | Google | https://arxiv.org/abs/2103.16193v1 | WER for Arabic | 0.4464 |
Speech Recognition | MediaSpeech | Google | https://arxiv.org/abs/2103.16193v1 | WER for French | 0.2385 |
Speech Recognition | MediaSpeech | Google | https://arxiv.org/abs/2103.16193v1 | WER for Turkish | 0.2707 |
Speech Recognition | MediaSpeech | Google | https://arxiv.org/abs/2103.16193v1 | WER for Spanish | 0.2176 |
Speech Recognition | MediaSpeech | wav2vec | https://arxiv.org/abs/2103.16193v1 | WER for Arabic | 0.9596 |
Speech Recognition | MediaSpeech | wav2vec | https://arxiv.org/abs/2103.16193v1 | WER for French | 0.3113 |
Speech Recognition | MediaSpeech | wav2vec | https://arxiv.org/abs/2103.16193v1 | WER for Turkish | 0.5812 |
Speech Recognition | MediaSpeech | wav2vec | https://arxiv.org/abs/2103.16193v1 | WER for Spanish | 0.2469 |
Speech Recognition | MediaSpeech | Deepspeech | https://arxiv.org/abs/2103.16193v1 | WER for French | 0.4741 |
Speech Recognition | MediaSpeech | Deepspeech | https://arxiv.org/abs/2103.16193v1 | WER for Spanish | 0.4236 |
Speech Recognition | MediaSpeech | Silero | https://arxiv.org/abs/2103.16193v1 | WER for Spanish | 0.3070 |
Speech Recognition | Common Voice English | parakeet-rnnt-1.1b | https://arxiv.org/abs/2305.05084v6 | Word Error Rate (WER) | 5.8% |
Speech Recognition | Common Voice English | Whisper (Large v2) | https://arxiv.org/abs/2212.04356v1 | Word Error Rate (WER) | 9.4% |
Speech Recognition | Common Voice vi | khanhld/chunkformer-large-vie | https://arxiv.org/abs/2502.14673v1 | Test WER | 6.66 |
Speech Recognition | Common Voice vi | wav2vec2-base-vietnamese-160h (No Language Model) | https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h | Test WER | 10.78 |
Speech Recognition | Common Voice vi | Vietnamese end-to-end speech recognition using wav2vec 2.0 by VietAI | https://github.com/vietai/ASR | Test WER | 11.52 |
Speech Recognition | Common Voice Frisian | wav2vec2-large-xls-r-1b-frisian | https://drive.google.com/file/d/1CAbwTxsabcRKH7UJwYJyxD1F32cjhkJb/view?usp=share_link | Test WER | 15.99% |
Speech Recognition | SPGISpeech | Icefall - zipformer transducer | null | Word Error Rate (WER) | 2.35 |
Speech Recognition | SPGISpeech | parakeet-rnnt-1.1b | https://arxiv.org/abs/2305.05084v6 | Word Error Rate (WER) | 3.11 |
Speech Recognition | SPGISpeech | Conformer | https://arxiv.org/abs/2104.02014v2 | Word Error Rate (WER) | 5.7 |
Speech Recognition | CALLHOME En | WavLM Large & EEND-vector clustering | https://arxiv.org/abs/2110.13900v5 | Word Error Rate (WER) | 10.35 |
Speech Recognition | Libri-Light test-clean | wav2vec 2.0 Large-10h-LV-60k | https://arxiv.org/abs/2006.11477v3 | Word Error Rate (WER) | 2.5 |
Speech Recognition | Libri-Light test-clean | TDS 60k pseudo-label + CTC fine-tuning + 4gram-LM | https://arxiv.org/abs/1912.07875v1 | Word Error Rate (WER) | 29.3 |
Speech Recognition | Libri-Light test-clean | CPC unlab-60k+train-10h CPC pretrain + CTC fine-tuning + 4gram-LM | https://arxiv.org/abs/1912.07875v1 | Word Error Rate (WER) | 43.9 |
Speech Recognition | Libri-Light test-clean | CPC unlab-60k | https://arxiv.org/abs/1912.07875v1 | ABX-within | 5.83 |
Speech Recognition | Libri-Light test-clean | CPC unlab-60k | https://arxiv.org/abs/1912.07875v1 | ABX-across | 7.56 |
Speech Recognition | Libri-Light test-clean | S6000h-n42-τ2 → 0.1 | https://arxiv.org/abs/2005.14578v1 | ABX-within | 9.33 |
Speech Recognition | Libri-Light test-clean | S6000h-n42-τ2 → 0.1 | https://arxiv.org/abs/2005.14578v1 | ABX-across | 13.53 |
Speech Recognition | EasyCom | ReVISE (bf) | https://arxiv.org/abs/2212.11377v1 | WER (%) | 52.1 |
Speech Recognition | EasyCom | ReVISE (ch2) | https://arxiv.org/abs/2212.11377v1 | WER (%) | 55.0 |
Speech Recognition | EasyCom | DAJA (MVDR,HMA,1000) (Overlapped Speech) | https://arxiv.org/abs/2207.07273v1 | WER (%) | 62.36 |
Speech Recognition | EasyCom | Demucs (bf) | https://arxiv.org/abs/2212.11377v1 | WER (%) | 69.8 |
Speech Recognition | EasyCom | Demucs (ch2) | https://arxiv.org/abs/2212.11377v1 | WER (%) | 86.8 |
Speech Recognition | TED-LIUM | Whisper-LLaMa-7b | https://arxiv.org/abs/2309.15701v2 | Word Error Rate (WER) | 4.6 |
Speech Recognition | TED-LIUM | ConformerXXL-PS | https://arxiv.org/abs/2109.13226v3 | Word Error Rate (WER) | 5 |
Speech Recognition | LibriSpeech test-clean | United Med ASR | https://arxiv.org/abs/2412.00055v1 | Word Error Rate (WER) | 0.985 |
Speech Recognition | LibriSpeech test-clean | SAMBA ASR | https://arxiv.org/abs/2501.02832v3 | Word Error Rate (WER) | 1.17 |
Speech Recognition | LibriSpeech test-clean | FAdam | https://arxiv.org/abs/2405.12807v10 | Word Error Rate (WER) | 1.34 |
Speech Recognition | LibriSpeech test-clean | Conformer + Wav2vec 2.0 + SpecAugment-based Noisy Student Training with Libri-Light | https://arxiv.org/abs/2010.10504v2 | Word Error Rate (WER) | 1.4 |
Speech Recognition | LibriSpeech test-clean | w2v-BERT XXL | https://arxiv.org/abs/2108.06209v2 | Word Error Rate (WER) | 1.4 |
Speech Recognition | LibriSpeech test-clean | parakeet-rnnt-1.1b | https://arxiv.org/abs/2305.05084v6 | Word Error Rate (WER) | 1.46 |
Speech Recognition | LibriSpeech test-clean | Conv + Transformer + wav2vec2.0 + pseudo labeling | https://arxiv.org/abs/2010.11430v1 | Word Error Rate (WER) | 1.5 |
Speech Recognition | LibriSpeech test-clean | ContextNet + SpecAugment-based Noisy Student Training with Libri-Light | https://arxiv.org/abs/2005.09629v2 | Word Error Rate (WER) | 1.7 |
Speech Recognition | LibriSpeech test-clean | SpeechStew (1B) | https://arxiv.org/abs/2104.02133v3 | Word Error Rate (WER) | 1.7 |
Speech Recognition | LibriSpeech test-clean | Multistream CNN with Self-Attentive SRU (WER includes text normalization) | https://arxiv.org/abs/2005.10469v1 | Word Error Rate (WER) | 1.75 |
Speech Recognition | LibriSpeech test-clean | Stateformer | https://arxiv.org/abs/2305.12498v2 | Word Error Rate (WER) | 1.76 |
Speech Recognition | LibriSpeech test-clean | wav2vec 2.0 with Libri-Light | https://arxiv.org/abs/2006.11477v3 | Word Error Rate (WER) | 1.8 |
Speech Recognition | LibriSpeech test-clean | HuBERT with Libri-Light | https://arxiv.org/abs/2106.07447v1 | Word Error Rate (WER) | 1.8 |
Speech Recognition | LibriSpeech test-clean | WavLM Large | https://arxiv.org/abs/2110.13900v5 | Word Error Rate (WER) | 1.8 |
Speech Recognition | LibriSpeech test-clean | E-Branchformer (L) + Internal Language Model Estimation | https://arxiv.org/abs/2210.00077v2 | Word Error Rate (WER) | 1.81 |
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