Speech dataset resources

This table summarizes dataset resources available

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Dataset name	Type of data	Annotation remarks	License	Metadata	Name/s of people who are entering the data	Who are you entering the data?
LibriSpeech	Audio books (clean, flac format)	Text is converted into upper-case, removed punctuation, expanding common abbreviations and non-standard words. The transcriptions are aligned and segmented automatically.	Creative Commons Attribution 4.0 International License.	Language: English The dataset is split into 3 sections with 100.6, 363.6, 496.7 hours of speech. The gender ratio of speakers is about half and half.	Dataset is made by Vassil Panayotov. Guoguo Chen, Daniel Povey, Sanjeev Khudanpur also contributed to the dataset and article below.	Alice Vanni Lifan Qu Ting Zhang Yilan Wei Siqi Zheng
LibriSpeech	LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.	Each book’s text is normalized by converting it into upper-case, re- moving the punctuation, and expanding common abbreviations and non-standard words.	CC BY 4.0	The audio recordings and transcriptions are all English The dataset is divided into different portions such as "train", "dev", and "test" to facilitate both training and evaluation The dataset is segmented into various sizes, denoted as "100" for 100 hours of audio, "360", and "500". The dataset is categorized into "clean" and "other" sets. The "clean" sets have high-quality recordings, while the "other" sets have recordings that might have more background noise or less clear pronunciations.	Prepared by Vassil Panayotov with the assistance of Daniel Povey	Yuxing Ouyang Xiaoling Lin Xueying Liu Jingxuan Yue M.Tepei The Part 2 of group work is on Librispeech.
LibriVoxDeEn	Audio based on audio books (.wav file format) German text and English translation (.tsv file format)	Low Disfluencies: The speech data in the dataset have a low level of disfluencies. Quality Evaluation: The quality of both audio and sentence alignments in the dataset has been assessed through manual evaluation. Sentence Alignment Quality: The quality of sentence alignments is stated to be comparable to widely-used parallel translation datasets.	Creative Commons Attribution 4.0 Non-Commercial ShareAlike Internation License	German audio and transcription, with English translation. >100 hours of audio material and >50k parallel sentences. Quality of audio and text has been evaluated manually.	Benjamin Beilharz, Xin Sun, Sariya Karimova, Stefan Riezler	Jocomin Galarneau & Ding Shenghuan & Ömer Tarik Özyilmaz
SPEECH-COCO	wav: spoken caption json: metadata of WAV file sqlite3: SQLite databases containing all the information contained in the JSON files	Using SOX tempo command, change the speed without changing the pitch to make the captions sound more natural. 1/3 of the captions are 10% slower than the original pace, 1/3 are 10% faster. Modify approximately 30% of the original captions and add disfluencies such as "um", "uh", "er" to make the captions more natural.	Creative Commons Attribution 4.0 International Liscence	Language: English This corpus contains 616,767 spoken captions from MSCOCO's val2014 and train2014 subsets 8 different voices. 4 of them have a British accent and the 4 others with American accent.	William Havard, Laurent Besacier, Olivier Rosec	Yining Lei Jingwen Shi Yanhua Liao Weixi Lai
Europarl-ASR	based on audio books (.wav	Official non-verbatim transcription of the speech, as a txt raw transcription file, as dfxp or srt force-aligned timed subtitle files, and its json metadata. Automatically filtered transcription of the speech, as dfxp or srt force-aligned timed subtitle files, and its json metadata. Automatically verbatimized transcription of the speech, as a txt transcription file, as dfxp or srt timed subtitle files, and its json metadata. Manually revised verbatim transcription of the speech, as a txt transcription file, as dfxp or srt timed subtitle files, and its json metadata.	CC BY 4.0	1300 hours of English-language annotated speech data. 3 full sets of timed transcriptions: official non-verbatim transcriptions, automatically noise-filtered transcriptions and automatically verbatimized transcriptions. 18 hours of speech data with both manually revised verbatim transcriptions and official non-verbatim transcriptions, split in 2 independent validation-evaluation partitions for 2 realistic ASR tasks (with vs. without previous knowledge of the speaker).	Garcés Díaz-Munío, Gonçal V.; Silvestre-Cerdà, Joan Albert; Jorge, Javier; Giménez, Adrià; Iranzo-Sánchez, Javier; Baquero-Arnal, Pau; Roselló, Nahuel; Pérez-González-de-Martos, Alejandro; Civera, Jorge; Sanchis, Albert; Juan, Alfons	Qing Li Weihao Jiang Yinqiu Wang Youyang Cai Ziyun Zhang
ASR-ETELECSC	WAV (PCM) TXT (UTF-8) English conversational speech beyond telephony	Speakers' gender metioned Languages mentioned Start and end time of speech indicated Speakers sequenced as numbers [1]/[2] Symbol description: [NOISE] for distinct ambient noises [UNKNOWN] for inaudible words or sentences, or a long passage of a foreign language [TTS] for synthetic voice [~] for readback or fragments [+] for overlapping speech [*] for unintelligible words or sentences [LAUGHTER] for laughter [PII] for Personally Identifiable Information	MAGIC DATA OPEN-SOURCE LICENSE	Total Duration: 5.04h Language: EN Speech Style: spontaneous conversation Audio parameters: 16 kHz, 16 bits, mono Recording Equipment: Telephony Recording Environment: Indoor Environment	Beijing Magic Data Technology Co., Ltd.	Cantao Su Chenyu Li Yanpei Ouyang Yi Lei
AliMeeting (Multi-Channel Multi-Party Meeting Transcription Challenge)	Recorded in multi-channel format for diarization, the default audio format is .wav. All transcripts of each meeting are stored in .TextGrid format	The annotation is very accurate, but uniquely formeetings like the AliMeeting data, speaker overlap should be explicitly addressed, this question still need to improve.	Creative Commons Attribution ShareAlike 4.0 International License.	Language: Mandarin Duration: 118.75 hours of voice data, including 104.75 hours of training set (Train), 4 hours of validation set (Eval), and 10 hours of test set (Test) Number of talkers: 456 (Male: 246, Female: 210) Environment: 13 different conference rooms, divided into three types according to size: small, medium and large, with room areas ranging from 8 to 55 square meters	Fan Yu, Shiliang Zhang, Pengcheng Guo, Yihui Fu, Zhihao Du, Siqi Zheng, Weilong Huang, Lei Xie, Zheng-Hua Tan, DeLiang Wang, Yanmin Qian, Kong Aik Lee, Zhijie Yan, Bin Ma, Xin Xu, Hui Bu	Dongwen Zhu & Yaling Deng & Chenyi Lin & Soogyeong Shin
TED-LIUM 3	2351 audio talks in NIST sphere format (SPH) 452 hours of audio 2351 aligned automatic transcripts in STM format TEDLIUM 2 dev and test data: 19 TED talks in SPH format with corresponding manual transcriptions Dictionary with pronunciations (159848 entries) Selected monolingual data for language modeling from WMT12 publicly available corpora		Creative Commons BY-NC-ND 3.0.	Language: English Transcription: Yes ( format: stm) Duration: 452 hrs Number of talkers: 1938 (Male: 1303; Female: 635) Alignments: cover around 83.0% of audio; 3.2M words Access: freely available for the research community	This new TED-LIUM release was made through a collaboration between the Ubiqus company and the LIUM (University of Le Mans, France). The LIUM team released two versions (respectively 118 hours of audio and 207 hours of audio) from the TED conference videos. Ubiqus joined these efforts to pursue the improvements both from an increased data standpoint, as well as from a technical achievement one.	Annie Zhou, XInyi Ma, Jingsi Huang, Igor, Wansu Zhu
Voxpopuli			Creative Commons Attribution-NonCommercial 4.0 International Public License	Languages: Bulgarian (Bg), Czech (Cs), Croatian (Hr), Danish (Da), Dutch (Nl), English (En), Estonian (Et), Finnish (Fi), French (Fr), German (De), Greek (El), Hungarian (Hu), Italian (It), Latvian (Lv), Lithuanian (Lt), Maltese (Mt), Polish (Pl), Portuguese (Pt), Romanian (Ro), Slovak (Sk), Slovene (Sl), Spanish (Es) and Swedish (Sv). Duration: 400K hours	Changhan Wang Morgane Riviere Ann Lee, Anne Wu Chaitanya Talnikar, Daniel Haziza, Mary Williamson Juan Pino, Emmanuel Dupoux	Amber Brandi MengJun Sherry Yu-Ting Yeh Erin

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Notes on LibriSpeech

• We only used the development set to test our ASR code in Python;
• The names of the speakers who recorded all the audiobooks contained in this corpus are also available in a separate text file;
• Exhaustive information about this dataset can be found in this article;
• This dataset was included in the Kaldi speech recognition toolkit.
• Another version of research on LibriSpeech by Yuxing Ouyang, Xiaoling Lin, Xueying Liu, Jingxuan Yue, M.Tepei can be found in Librispeech.