OLAC Record

Title:2018 NIST Speaker Recognition Evaluation Test Set
Access Rights:Licensing Instructions for Subscription & Standard Members, and Non-Members: http://www.ldc.upenn.edu/language-resources/data/obtaining
Bibliographic Citation:Greenberg, Craig, et al. 2018 NIST Speaker Recognition Evaluation Test Set LDC2020S04. Web Download. Philadelphia: Linguistic Data Consortium, 2020
Contributor:Greenberg, Craig
Sadjadi, Omid
Singer, Elliot
Walker, Kevin
Jones, Karen
Wright, Jonathan
Strassel, Stephanie
Date (W3CDTF):2020
Date Issued (W3CDTF):2020-04-15
Description:*Introduction* 2018 NIST Speaker Recognition Evaluation Test Set was developed by the Linguistic Data Consortium (LDC) and NIST (National Institute of Standards and Technology). It contains approximately 396 hours of Tunisian Arabic telephone recordings and English web video speech used as development and test data in the NIST-sponsored 2018 Speaker Recognition Evaluation (SRE). The ongoing series of SRE yearly evaluations conducted by NIST are intended to be of interest to researchers working on the general problem of text independent speaker recognition. To this end the evaluations are designed to be simple, to focus on core technology issues, to be fully supported and to be accessible to those wishing to participate. The SRE task is speaker detection, that is, to determine whether a specified target speaker is speaking during a segment of speech. In addition to the traditional focus on telephone speech recorded over a variety of handset types for the training and test conditions, SRE18 added voice over IP data and audio from video. Further information about the evaluation, including the features added in SRE18, is contained in the evaluation plan included in this release. *Data* The telephone speech data was drawn from the Call My Net 2 (CMN2) collection conducted by LDC in Tunisia in which Tunisian Arabic speakers called friends or relatives who agreed to record their telephone conversations lasting between 8-10 minutes. The speech segments include PSTN (public switched telephone network) and VOIP (voice over IP) data. The English audio was sampled from amateur web videos collected by LDC as part of the Video Annotation for Speech Technology (VAST) project. Telephone speech is presented as 8 bit a-law with a sample rate of 8000. The VAST data are presented as 16 bit FLAC files sampled at 44 kHz. In addition to development and evaluation data, this corpus also contains answer keys, trial and train files, development data and evaluation documentation. *Samples* Please view this telephone sample (SPH) and audio from video sample (FLAC). *Updates* None at this time.
Extent:Corpus size: 13281597 KB
Format:Sampling Rate: 8000
Sampling Format: alaw
ISBN: 1-58563-923-0
ISLRN: 092-864-780-386-1
DOI: 10.35111/secv-qh25
Tunisian Arabic
Language (ISO639):eng
License:LDC User Agreement for Non-Members: https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf
Medium:Distribution: Web Download
Publisher:Linguistic Data Consortium
Publisher (URI):https://www.ldc.upenn.edu
Relation (URI):https://catalog.ldc.upenn.edu/docs/LDC2020S04
Rights Holder:Portions © 2011-2018 YouTube, LLC, © 2020 Trustees of the University of Pennsylvania
Type (DCMI):Sound
Type (OLAC):primary_text


Archive:  The LDC Corpus Catalog
Description:  http://www.language-archives.org/archive/www.ldc.upenn.edu
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OaiIdentifier:  oai:www.ldc.upenn.edu:LDC2020S04
DateStamp:  2021-01-01
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Citation: Greenberg, Craig; Sadjadi, Omid; Singer, Elliot; Walker, Kevin; Jones, Karen; Wright, Jonathan; Strassel, Stephanie. 2020. Linguistic Data Consortium.
Terms: area_Africa area_Europe country_GB country_TN dcmi_Sound iso639_aeb iso639_eng olac_primary_text

Up-to-date as of: Sun Jun 16 7:34:58 EDT 2024