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ReDimNet2+: Multi-Corpus Data Scaling for Robust Speaker Verification

2026-09-29 08:34:01
Kirill Borodin, Vasilii Kudryavtsev, Maxim Maslov, Grach Mkrtchian
arXiv_SD
 
Abstract

Automatic speaker verification must remain reliable across devices, rooms, and compression pipelines. We present ReDimNet2+, which scales training of the compact ReDimNet2 backbone across seven public corpora (63,934 speakers, about 8,675 hours). Analysis of a VoxBlink2 subset reveals a shift in predicted spectral coloration, motivating codec and waveform augmentation alongside this multi-corpus training, large-margin fine-tuning (LMFT), and graph-based retrieval reranking. With random 4-second evaluation windows for all models, ReDimNet2+ LMFT reduces pooled VoxCeleb1 EER from 2.42% to 0.82% and a 26-condition robustness stress-test EER from 7.21% to 1.99%. Under this shared local protocol, it reaches 0.35% EER on VoxCeleb1-O versus 0.787% for the best evaluated WeSpeaker checkpoint. On a VoxBlink2 retrieval subset, reranking improves the final model's Pr@k from 0.7413 to 0.7687.

Abstract (translated)

URL

https://arxiv.org/abs/2609.37014

PDF

https://arxiv.org/pdf/2609.37014.pdf

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