OLAC Record oai:www.ldc.upenn.edu:LDC2023S03

Metadata
Title:		2019 NIST Speaker Recognition Evaluation Test Set -- CTS Challenge
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. 2019 NIST Speaker Recognition Evaluation Test Set -- CTS Challenge LDC2023S03. Web Download. Philadelphia: Linguistic Data Consortium, 2023
Contributor:		Greenberg, Craig
		Sadjadi, Omid
		Singer, Elliot
		Walker, Kevin
		Jones, Karen
		Caruso, Christopher
		Wright, Jonathan
		Strassel, Stephanie
Date (W3CDTF):		2023
Date Issued (W3CDTF):		2023-05-15
Description:		*Introduction* 2019 NIST Speaker Recognition Evaluation Test Set -- CTS Challenge was developed by the Linguistic Data Consortium (LDC) and NIST (National Institute of Standards and Technology). It contains approximately 635 hours of Tunisian Arabic telephone recordings for development and test, answer keys, enrollment, trial files and documentation from the CTS Challenge portion of the NIST-sponsored 2019 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 2019 evaluation task was speaker detection, that is, to determine whether a specified target speaker was speaking during a segment of speech. The evaluation was conducted in two parts: (1) a leaderboard-style challenge based on conversational telephone speech from LDC's Call My Net 2 (CMN2) corpus; and (2) a separate evaluation using audio-visual material collected by LDC for the VAST (Video Annotation for Speech Technology) project. Further information about the evaluation is contained in the evaluation plan included in this release. *Data* The telephone speech data for the CTS Challenge was drawn from the 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. This telephone speech is presented in sphere format as 8-bit a-law with a sample rate of 8000 KHz. *Samples* Please see the following audio sample. *Updates* None at this time.
Extent:		Corpus size: 15994208 KB
Format:		Sampling Rate: 8000Hz
		Sampling Format: alaw
Identifier:		LDC2023S03
		https://catalog.ldc.upenn.edu/LDC2023S03
		ISLRN: 699-091-998-273-0
		DOI: 10.35111/7r78-ra45
Language:		Tunisian Arabic
Language (ISO639):		aeb
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/LDC2023S03
Rights Holder:		Portions © 2019, 2020, 2023 Trustees of the University of Pennsylvania
Type (DCMI):		Sound
Type (OLAC):		primary_text
OLAC Info
Archive:		The LDC Corpus Catalog
Description:		http://www.language-archives.org/archive/www.ldc.upenn.edu
GetRecord:		OAI-PMH request for OLAC format
GetRecord:		Pre-generated XML file
OAI Info
OaiIdentifier:		oai:www.ldc.upenn.edu:LDC2023S03
DateStamp:		2023-11-17
GetRecord:		OAI-PMH request for simple DC format
Search Info
Citation:		Greenberg, Craig; Sadjadi, Omid; Singer, Elliot; Walker, Kevin; Jones, Karen; Caruso, Christopher; Wright, Jonathan; Strassel, Stephanie. 2023. Linguistic Data Consortium.
Terms:		area_Africa country_TN dcmi_Sound iso639_aeb olac_primary_text