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| https://openslr.org/38/ | 24 | 25 | **English** 26 | | name | duration/h | address | remark 27 | | --- | --- | --- | --- 28 | | Common Voice | 2015 | https://commonvoice.mozilla.org/zh-CN/datasets | Common Voice Corpus 7.0 29 | | LibriSpeech | 960 | https://openslr.org/12/ | 30 | | ST-AEDS-20180100 | 4.7 | http://www.openslr.org/45/ | 31 | | TED-LIUM Release 3 | 430 | https://openslr.org/51/ | 32 | | Multilingual LibriSpeech | 44659 | https://openslr.org/94/ | limited supervision 33 | | SPGISpeech | 5000 | https://datasets.kensho.com/datasets/scribe | if available 34 | | Speech Commands | 10 | https://www.kaggle.com/c/tensorflow-speech-recognition-challenge/data | 35 | | 2020AESRC | 160 | https://datatang.com/INTERSPEECH2020 | if available 36 | | GigaSpeech | 10000 | https://github.com/SpeechColab/GigaSpeech | 37 | | The People’s Speech | 31400 | https://openreview.net/pdf?id=R8CwidgJ0yT | 38 | | Earnings-21 | 39 | https://arxiv.org/abs/2104.11348 | 39 | | VoxPopuli | 24100+543 | https://arxiv.org/pdf/2101.00390.pdf | 24100(unlabeled), 543(transcribed) 40 | | CMU Wilderness Multilingual Speech Dataset | 13 | http://festvox.org/cmu_wilderness/ | Multilingual 41 | | MSR-86K | 9795.46 | https://huggingface.co/datasets/Alex-Song/MSR-86K | Multilingual 42 | 43 | **Chinese-English** 44 | | name | duration/h | address | remark 45 | | --- | --- | --- | --- 46 | | SEAME | 30 | https://www.isca-speech.org/archive_v0/archive_papers/interspeech_2010/i10_1986.pdf | 47 | | TAL CSASR | 587 | https://ai.100tal.com/dataset | 48 | | ASRU2019 CSASR | 200 | https://www.datatang.com/competition | if available 49 | | ASCEND | 10.62 | https://arxiv.org/pdf/2112.06223.pdf | 50 | 51 | **Japanese (ja-JP)** 52 | | name | duration/h | address | remark 53 | | --- | --- | --- | --- 54 | | Common Voice | 26 | https://commonvoice.mozilla.org/zh-CN/datasets | Common Voice Corpus 7.0 55 | | Japanese_Scripted_Speech_Corpus_Daily_Use_Sentence | 18 | https://magichub.io/cn/datasets/japanese-scripted-speech-corpus-daily-use-sentence/ | 56 | | LaboroTVSpeech | 2000 | https://arxiv.org/pdf/2103.14736.pdf | 57 | | CSJ | 650 | https://github.com/kaldi-asr/kaldi/tree/master/egs/csj | 58 | | JTubeSpeech | 1300 | https://arxiv.org/pdf/2112.09323.pdf 59 | | MSR-86K | 1779.03 | https://huggingface.co/datasets/Alex-Song/MSR-86K | Multilingual 60 | 61 | **Korean (ko-KR)** 62 | | name | duration/h | address | remark 63 | | --- | --- | --- | --- 64 | | korean-scripted-speech-corpus-daily-use-sentence | 4.3 | https://magichub.io/cn/datasets/korean-scripted-speech-corpus-daily-use-sentence/ | 65 | | korean-conversational-speech-corpus | 5.22 | https://magichub.io/cn/datasets/korean-conversational-speech-corpus/ | 66 | | MSR-86K | 10338.66 | https://huggingface.co/datasets/Alex-Song/MSR-86K | Multilingual 67 | 68 | **Russian (ru-RU)** 69 | | name | duration/h | address | remark 70 | | --- | --- | --- | --- 71 | | Common Voice | 148 | https://commonvoice.mozilla.org/zh-CN/datasets | Common Voice Corpus 7.0 72 | | OpenSTT | 20000 | https://arxiv.org/pdf/2006.08274.pdf | limited supervision 73 | | MSR-86K | 3188.52 | https://huggingface.co/datasets/Alex-Song/MSR-86K | Multilingual 74 | 75 | **French (fr-Fr)** 76 | | name | duration/h | address | remark 77 | | --- | --- | --- | --- 78 | | MediaSpeech | 10 | https://arxiv.org/pdf/2103.16193.pdf | ASR system evaluation dataset 79 | | MSR-86K | 8316.70 | https://huggingface.co/datasets/Alex-Song/MSR-86K | Multilingual 80 | 81 | **Spanish (es-ES)** 82 | | name | duration/h | address | remark 83 | | --- | --- | --- | --- 84 | | MediaSpeech | 10 | https://arxiv.org/pdf/2103.16193.pdf | ASR system evaluation dataset 85 | | MSR-86K | 13976.84 | https://huggingface.co/datasets/Alex-Song/MSR-86K | Multilingual 86 | 87 | **Turkish (tr-TR)** 88 | | name | duration/h | address | remark 89 | | --- | --- | --- | --- 90 | | MediaSpeech | 10 | https://arxiv.org/pdf/2103.16193.pdf | ASR system evaluation dataset 91 | 92 | **Arabic (ar)** 93 | | name | duration/h | address | remark 94 | | --- | --- | --- | --- 95 | | MediaSpeech | 10 | https://arxiv.org/pdf/2103.16193.pdf | ASR system evaluation dataset 96 | | MSR-86K | 873.84 | https://huggingface.co/datasets/Alex-Song/MSR-86K | Multilingual 97 | 98 | **noise & nonspeech** 99 | | name | duration/h | address | remark 100 | | --- | --- | --- | --- 101 | | MUSAN | - | https://openslr.org/17/ | 102 | | Room Impulse Response and Noise Database | - | https://openslr.org/28/ | 103 | | AudioSet | - | https://ieeexplore.ieee.org/document/7952261 | 104 | 105 | --------------------------------------------------------------------------------------------------------------------- 106 | --------------------------------------------------------------------------------------------------------------------- 107 | 108 | ## The Dataset of Speech Synthesis 109 | 110 | **Chinese** 111 | | name | duration/h | address | remark 112 | | --- | --- | --- | --- 113 | | Aishell3 | 85 | https://openslr.org/93/ | 114 | | Opencpop | - | https://wenet.org.cn/opencpop/download/ | Singing Voice Synthesis 115 | 116 | **English** 117 | | name | duration/h | address | remark 118 | | --- | --- | --- | --- 119 | | Hi-Fi Multi-Speaker English TTS Dataset | 291.6 | https://openslr.org/109/ | 120 | | LibriTTS corpus | 585 | https://openslr.org/60/ | 121 | | Speechocean762 | - | https://www.openslr.org/101/ | 122 | | RyanSpeech | 10 | http://mohammadmahoor.com/ryanspeech/ | 123 | 124 | --------------------------------------------------------------------------------------------------------------------- 125 | --------------------------------------------------------------------------------------------------------------------- 126 | 127 | ## The Dataset of Speech Recognition & Speaker Diarization 128 | **Chinese** 129 | | name | duration/h | address | remark | application 130 | | --- | --- | --- | --- | --- 131 | | Aishell4 | 120 | https://openslr.org/111/ | 8-channel, conference scenarios | speech recognition, speaker diarization 132 | | ASR&SD | 160 | http://ncmmsc2021.org/competition2.html | if available | speech recognition, speaker diarization 133 | | zhijiangcup | - | https://zhijiangcup.zhejianglab.com/zhijiang/match/details/id/6.html | if available | speech recognition, speaker diarization 134 | | M2MET | 120 | https://arxiv.org/pdf/2110.07393.pdf | 8-channel, conference scenarios | speech recognition, speaker diarization 135 | 136 | **English** 137 | | name | duration/h | address | remark | application 138 | | --- | --- | --- | --- | --- 139 | | CHiME-6 | - | https://chimechallenge.github.io/chime6/download.html | if available | speech recognition, speaker diarization 140 | 141 | --------------------------------------------------------------------------------------------------------------------- 142 | --------------------------------------------------------------------------------------------------------------------- 143 | 144 | ## The Dataset of Speaker Recognition 145 | **Chinese** 146 | | name | duration/h | address | remark | application 147 | | --- | --- | --- | --- | --- 148 | | CN-Celeb | - | https://openslr.org/82/ | 149 | | KeSpeech | 1542 | https://openreview.net/forum?id=b3Zoeq2sCLq | | speech recognition, speaker verification, subdialect identification, voice conversion 150 | | MTASS | 55.6 | https://github.com/Windstudent/Complex-MTASSNet | | 151 | | THCHS-30 | 40 | http://www.openslr.org/18/ | | 152 | 153 | **English** 154 | | name | duration/h | address | remark 155 | | --- | --- | --- | --- 156 | | VoxCeleb Data | - | http://www.robots.ox.ac.uk/~vgg/data/voxceleb/ | 157 | 158 | ## The Dataset of Voice Activity Detection 159 | **French** 160 | | name | duration/h | address | remark | application 161 | | --- | --- | --- | --- | --- 162 | | InaGVAD | 5 | https://github.com/ina-foss/InaGVAD | 10 radio and 18 TV channels| Voice Activity Detection, Speaker Gender Segmentation, Gender Monitoring 163 | -------------------------------------------------------------------------------- /paper.md: -------------------------------------------------------------------------------- 1 | ## paper list 2 | 3 | - 2019.05, CMU Wilderness Multilingual Speech Dataset, [CMU Wilderness Multilingual Speech Dataset](https://ieeexplore.ieee.org/document/8683536) 4 | 5 | - 2010.03, a Mandarin-English Code-switching Speech Corpus in South-East Asia, [SEAME: a Mandarin-English Code-switching Speech Corpus in South-East Asia](https://www.isca-speech.org/archive_v0/archive_papers/interspeech_2010/i10_1986.pdf) 6 | 7 | - 2020.06, largest open-source Russian language dataset, [Exploration of End-to-End ASR for OpenSTT–Russian Open Speech-to-Text Dataset](https://arxiv.org/pdf/2006.08274.pdf) 8 | 9 | - 2020,12, LARGE-SCALE MULTILINGUAL DATASET, [MLS: A LARGE-SCALE MULTILINGUAL DATASET FOR SPEECH RESEARCH](https://arxiv.org/abs/2012.03411) 10 | 11 | - 2021.01, Multilingual Speech Corpus, [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/pdf/2101.00390.pdf) 12 | 13 | - 2021.04, a new multi-speaker English dataset for training text-to-speech models, [Hi-Fi Multi-Speaker English TTS Dataset](https://arxiv.org/abs/2104.01497) 14 | 15 | - 2021.04, professionally transcribed earnings calls, [SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition](https://arxiv.org/abs/2104.02014) 16 | 17 | - 2021.06, multi-domain English speech recognition corpus, [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://arxiv.org/abs/2106.06909) 18 | 19 | - 2021.07, a new multi-task audio source separation (MTASS) challenge, [MULTI-TASK AUDIO SOURCE SEPARATION](https://arxiv.org/pdf/2107.06467.pdf) 20 | 21 | - 2021.08, a new speech corpus for research on automated text-to-speech (TTS) systems, [RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis](https://arxiv.org/abs/2106.08468) 22 | 23 | - 2021.10, multi-domain Mandarin corpus consisting of 10000+ hours high-quality labeled speech, [WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition](https://arxiv.org/abs/2110.03370) 24 | 25 | - 2021.10, 120 hours of real recorded Mandarin meeting data, [M2MET: THE ICASSP 2022 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION CHALLENGE](https://arxiv.org/pdf/2110.07393.pdf) 26 | 27 | - 2021.11, Large-Scale Diverse English Dataset, [The People’s Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage](https://openreview.net/pdf?id=R8CwidgJ0yT) 28 | 29 | - 2021.11, 1,542 hours of speech signals recorded by 27,237 speakers in 34 cities in China, [KeSpeech: An Open Source Speech Dataset of Mandarin and Its Eight Subdialects](https://openreview.net/forum?id=b3Zoeq2sCLq) 30 | 31 | - 2021.12, ASCEND (A Spontaneous Chinese-English Dataset), [ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation](https://arxiv.org/pdf/2112.06223.pdf) 32 | 33 | - 2021.12, a large-scale Japanese ASR benchmark with more than 1,300 hours of data, [JTubeSpeech: corpus of Japanese speech collected from YouTube for speech recognition and speaker verification](https://arxiv.org/abs/2112.09323) 34 | 35 | - 2022.01, a publicly available highquality Mandarin singing corpus designed for singing voice synthesis, [Opencpop: A High-Quality Open Source Chinese Popular Song Corpus for Singing Voice Synthesis](https://arxiv.org/pdf/2201.07429.pdf) 36 | 37 | - 2022.06, a new corpus of Mandarin-English code-switching speech recognition, [TALCS: AN OPEN-SOURCE MANDARIN-ENGLISHCODE-SWITCHINGCORPUS AND A SPEECH RECOGNITION BASELINE](https://arxiv.org/pdf/2206.13135.pdf) 38 | 39 | - 2024.06, a large-scale, multi-domain, multilingual speech recognition corpus, [GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement](https://arxiv.org/pdf/2406.11546) 40 | 41 | - 2024.06, an evolving, large-scale multilingual corpus for speech recognition research, [MSR-86K:An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research](https://www.arxiv.org/pdf/2406.18301) 42 | 43 | --------------------------------------------------------------------------------