{"url":"/sota/speech-enhancement-on-easycom","task":{"name":"Speech Enhancement","url":"/task/speech-enhancement","note":null},"dataset":{"name":"EasyCom","url":"/dataset/easycom"},"category":"Audio","categories":["Audio","Speech"],"category_note":null,"description":"**Speech Enhancement** is a signal processing task that involves improving the quality of speech signals captured under noisy or degraded conditions. The goal of speech enhancement is to make speech signals clearer, more intelligible, and more pleasant to listen to, which can be used for various applications such as voice recognition, teleconferencing, and hearing aids. A representative Github project with online demo : [ClearerVoice-Studio](https://github.com/modelscope/ClearerVoice-Studio).\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [A Fully Convolutional Neural Network For Speech Enhancement](https://arxiv.org/pdf/1609.07132v1.pdf) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["PESQ","STOI","ViSQOL","HASQI","Audio Quality MOS","SDR","ESTOI","HASPI","SI-SDR","SIIB","SNR","SegSNR"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"PESQ":null,"STOI":null,"ViSQOL":null,"HASQI":null,"Audio Quality MOS":null,"SDR":null,"ESTOI":null,"HASPI":null,"SI-SDR":null,"SIIB":null,"SNR":null,"SegSNR":null}},"counts":{"rows":6,"rows_with_code":1,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MaxDI (Baseline)","metrics":{"ESTOI":"0.379","HASPI":"0.830","HASQI":"0.249","PESQ":"1.17","SDR":"-12.9","SI-SDR":"-23.4","SIIB":"139","SNR":"-10.1","STOI":"0.544","SegSNR":"-12.2","ViSQOL":"1.68"},"uses_additional_data":false,"paper_date":"2021-07-09","paper":"/paper/easycom-an-augmented-reality-dataset-to","paper_url":"https://arxiv.org/abs/2107.04174v2","paper_title":"EasyCom: An Augmented Reality Dataset to Support Algorithms for Easy Communication in Noisy Environments","code":"https://github.com/facebookresearch/EasyComDataset","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"ReVISE (ch2)","metrics":{"Audio Quality MOS":"4.19"},"uses_additional_data":false,"paper_date":"2022-12-21","paper":"/paper/revise-self-supervised-speech-resynthesis","paper_url":"https://arxiv.org/abs/2212.11377v1","paper_title":"ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":3,"model":"ReVISE (bf)","metrics":{"Audio Quality MOS":"4.11"},"uses_additional_data":false,"paper_date":"2022-12-21","paper":"/paper/revise-self-supervised-speech-resynthesis","paper_url":"https://arxiv.org/abs/2212.11377v1","paper_title":"ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":4,"model":"Demucs (ch2)","metrics":{"Audio Quality MOS":"2.95"},"uses_additional_data":false,"paper_date":"2022-12-21","paper":"/paper/revise-self-supervised-speech-resynthesis","paper_url":"https://arxiv.org/abs/2212.11377v1","paper_title":"ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"Demucs (bf)","metrics":{"Audio Quality MOS":"2.39"},"uses_additional_data":false,"paper_date":"2022-12-21","paper":"/paper/revise-self-supervised-speech-resynthesis","paper_url":"https://arxiv.org/abs/2212.11377v1","paper_title":"ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":6,"model":"DAJA (MVDR,HMA,1000) (Overlapped Speech)","metrics":{"SDR":"-4.76"},"uses_additional_data":false,"paper_date":"2022-07-15","paper":"/paper/direction-aware-joint-adaptation-of-neural","paper_url":"https://arxiv.org/abs/2207.07273v1","paper_title":"Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments","code":null,"n_code_links":0,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. 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