NIST SRE CTS Superset: A large-scale dataset for telephony speaker recognition
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Abstract (arXiv)
This document provides a brief description of the National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) conversational telephone speech (CTS) Superset. The CTS Superset has been created in an attempt to provide the research community with a large-scale dataset along with uniform metadata that can be used to effectively train and develop telephony (narrowband) speaker recognition systems. It contains a large number of telephony speech segments from more than 6800 speakers with speech durations distributed uniformly in the [10s, 60s] range. The segments have been extracted from the source corpora used to compile prior SRE datasets (SRE1996-2012), including the Greybeard corpus as well as the Switchboard and Mixer series collected by the Linguistic Data Consortium (LDC). In addition to the brief description, we also report speaker recognition results on the NIST 2020 CTS Speaker Recognition Challenge, obtained using a system trained with the CTS Superset. The results will serve as a reference baseline for the challenge.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 3 of its 11 references that are in the map. First posted on arXiv 16 Aug 2021.
Builds on
3 of 11 references.
References · 3
- 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
- 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 968
- 2015MUSAN: A Music, Speech, and Noise Corpuscited by 400
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Citing papers per year: 2022: 3; 2023: 1.
4 papers.
- 2023Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification22 May 2023
- 2022Toroidal Probabilistic Spherical Discriminant Analysis27 Oct 2022
- 2022The 2021 NIST Speaker Recognition Evaluation21 Apr 2022
- 2022The NIST CTS Speaker Recognition Challenge21 Apr 2022