Speech dataset resources

This table summarizes dataset resources available

Note: You have editing rights to the table, so you can edit/adjust it to your needs*. Your instructors will clean it up afterwards.

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 are also contributed to the dataset and article below.
Hugging Face
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			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
SAF (Short Answer Feedback Dataset)	based on audio books (.wav		CC BY-SA
ASR-ETELECSC	WAV (PCM) TXT (UTF-8)	Speakers' gender Noise/laughter marked Languages mentioned Starting and ending time of speech Speakers sequenced as numbers [1]/[2] Sound unrecognized as [UNKNOWN] Pause marked as [+] Incomplete words marked as [~] Ambiguity marked as [*]	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
Europarl-ASR	A speech and text corpus of parliamentary debates (audio format .m4a; transcription format: .txt; metadata: .csv)		Creative Commons Attribution 4.0 License.	Language : English 1300 hours of English annotated speech data 3 full sets of timed transcriptions 70 million tokens of English-language text data.	•Gonçal V. Garcés Díaz-Munío •Joan Albert Silvestre-Cerdà	Dongwen Zhu & Yaling Deng & Chenyi Lin & Soogyeong Shin
AliMeeting (Multi-Channel Multi-Party Meeting Transcription Challenge)				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)
TED-LIUM 3				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

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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.