2018 NIST Speaker Recognition Evaluation Test Set

Item Name: 2018 NIST Speaker Recognition Evaluation Test Set
Author(s): Craig Greenberg, Omid Sadjadi, Elliot Singer, Kevin Walker, Karen Jones, Jonathan Wright, Stephanie Strassel
LDC Catalog No.: LDC2020S04
ISBN: 1-58563-923-0
ISLRN: 092-864-780-386-1
DOI: https://doi.org/10.35111/secv-qh25
Release Date: April 15, 2020
Member Year(s): 2020
DCMI Type(s): Sound
Sample Type: alaw
Sample Rate: 8000
Data Source(s): telephone conversations, video
Project(s): NIST SRE
Application(s): speaker identification
Language(s): English, Tunisian Arabic
Language ID(s): eng, aeb
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2020S04 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Greenberg, Craig, et al. 2018 NIST Speaker Recognition Evaluation Test Set LDC2020S04. Web Download. Philadelphia: Linguistic Data Consortium, 2020.
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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.


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.


Please view this telephone sample (SPH) and audio from video sample (FLAC).


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