Analysis of ABC Frontend Audio Systems for the NIST-SRE24

Sara Barahona; Anna Silnova; Ladislav Mošner; Junyi Peng; Oldřich Plchot; Johan Rohdin; Lin Zhang; Jiangyu Han; Petr Palka; Federico Landini; Lukáš Burget; Themos Stafylakis; Sandro Cumani; Dominik Boboš; Miroslav Hlavaček; Martin Kodovsky; Tomáš Pavlíček

arXiv · 2505.15320

Analysis of ABC Frontend Audio Systems for the NIST-SRE24

Sara Barahona·Anna Silnova·Ladislav Mošner·Junyi Peng·Oldřich Plchot·Johan Rohdin·Lin Zhang·Jiangyu Han·Petr Palka·Federico Landini·Lukáš Burget·Themos Stafylakis·Sandro Cumani·Dominik Boboš·Miroslav Hlavaček·Martin Kodovsky·Tomáš Pavlíček

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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.

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Sara Barahona, Anna Silnova, Ladislav Mošner, Junyi Peng, Oldřich Plchot, Johan Rohdin, Lin Zhang, Jiangyu Han, Petr Palka, Federico Landini, Lukáš Burget, Themos Stafylakis, Sandro Cumani, Dominik Boboš, Miroslav Hlavaček, Martin Kodovsky, Tomáš Pavlíček. 2025-05-21. Analysis of ABC Frontend Audio Systems for the NIST-SRE24. https://arxiv.org/abs/2505.15320

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