2019 NIST Speaker Recognition Evaluation Test Set -- CTS Challenge
| Item Name: | 2019 NIST Speaker Recognition Evaluation Test Set -- CTS Challenge |
| Author(s): | Craig Greenberg, Omid Sadjadi, Elliot Singer, Kevin Walker, Karen Jones, Christopher Caruso, Jonathan Wright, Stephanie Strassel |
| LDC Catalog No.: | LDC2023S03 |
| ISLRN: | 699-091-998-273-0 |
| DOI: | https://doi.org/10.35111/7r78-ra45 |
| Release Date: | May 15, 2023 |
| Member Year(s): | 2023 |
| DCMI Type(s): | Sound |
| Sample Type: | alaw |
| Sample Rate: | 8000Hz |
| Data Source(s): | telephone speech |
| Project(s): | NIST SRE |
| Application(s): | speaker identification |
| Language(s): | Tunisian Arabic |
| Language ID(s): | aeb |
| License(s): |
LDC User Agreement for Non-Members |
| Online Documentation: | LDC2023S03 Documents |
| Licensing Instructions: | Subscription & Standard Members, and Non-Members |
| Citation: | Greenberg, Craig, et al. 2019 NIST Speaker Recognition Evaluation Test Set -- CTS Challenge LDC2023S03. Web Download. Philadelphia: Linguistic Data Consortium, 2023. |
| Related Works: | View |
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.