Speech dataset resources: Difference between revisions

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* Guoguo Chen , Daniel Povey , Sanjeev Khudanpur are also contributed to the dataset and article below.  
* Guoguo Chen , Daniel Povey , Sanjeev Khudanpur are also contributed to the dataset and article below.  
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* Alice Vanni
* Lifan Qu
* Ting Zhang
* Yilan Wei
* Siqi Zheng
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|[https://www.openslr.org/12 LibriSpeech]
|[https://www.openslr.org/12 LibriSpeech]

Revision as of 07:19, 28 September 2023

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.
  • 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.[1] Each book’s text is normalized by converting it into upper-case, re- moving the punctuation, and expanding common abbreviations and non-standard words.[2] 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[1]
  • Yuxing Ouyang
  • Xiaoling Lin
  • Xueying Liu
  • Jingxuan Yue
  • M.Tepei

The Part 2 of group work is on Librispeech.

Hugging Face
LibriVoxDeEn Audio based on audio books (.wav file format)

German text and English translation (.tsv file format)

  1. Low Disfluencies: The speech data in the dataset have a low level of disfluencies.
  2. Quality Evaluation: The quality of both audio and sentence alignments in the dataset has been assessed through manual evaluation.
  3. 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
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.[3]
Annie Zhou, XInyi Ma, Jingsi Huang, Igon,

*) this means: you may also add additional pages and link to them in this table

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.

References

  1. 1.0 1.1 https://www.openslr.org/12
  2. Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015, April). Librispeech: an asr corpus based on public domain audio books. In 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5206-5210). IEEE.
  3. Hernandez, François, Vincent Nguyen, Sahar Ghannay, Natalia Tomashenko, and Yannick Estève. “TED-LIUM 3: Twice as Much Data and Corpus Repartition for Experiments on Speaker Adaptation.” In Speech and Computer, edited by Alexey Karpov, Oliver Jokisch, and Rodmonga Potapova, 11096:198–208. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2018. https://doi.org/10.1007/978-3-319-99579-3_21.