Papers for

voice biometrics teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Speaker verification improves with multi-corpus training and data scaling

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

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.

Tue 29 SeptSound

The gist

Speaker verification systems check if a voice matches a specific person, but they can struggle with different recording devices or environments. The authors created ReDimNet2+, a method trained on many speech collections to make voice recognition more reliable under varied conditions. They combined techniques like sound data changes and fine-tuning to lower error rates significantly. Their approach also includes smart ways to pick the best voice matches from a large library.

Open → 2609.37014v1