Analysis of ABC Frontend Audio Systems for the NIST-SRE24
Abstract
We present a comprehensive analysis of the embedding extractors (frontends) developed by the ABC team for the audio track of NIST SRE 2024. We follow the two scenarios imposed by NIST: using only a provided set of telephone recordings for training (fixed) or adding publicly available data (open condition). Under these constraints, we develop the best possible speaker embedding extractors for the pre-dominant conversational telephone speech (CTS) domain. We explored architectures based on ResNet with different pooling mechanisms, recently introduced ReDimNet architecture, as well as a system based on the XLS-R model, which represents the family of large pre-trained self-supervised models. In open condition, we train on VoxBlink2 dataset, containing 110 thousand speakers across multiple languages. We observed a good performance and robustness of VoxBlink-trained models, and our experiments show practical recipes for developing state-of-the-art frontends for speaker recognition.
Keywords
Cite
@article{arxiv.2505.15320,
title = {Analysis of ABC Frontend Audio Systems for the NIST-SRE24},
author = {Sara Barahona and Anna Silnova and Ladislav Mošner and Junyi Peng and Oldřich Plchot and Johan Rohdin and Lin Zhang and Jiangyu Han and Petr Palka and Federico Landini and Lukáš Burget and Themos Stafylakis and Sandro Cumani and Dominik Boboš and Miroslav Hlavaček and Martin Kodovsky and Tomáš Pavlíček},
journal= {arXiv preprint arXiv:2505.15320},
year = {2025}
}
Comments
Accepted at Interspeech 2025