Portrait de Gaspard Botté

Gaspard Botté

gaspard.botte@mila.quebec
Collaborateur·rice de recherche - Concordia
Superviseur⋅e principal⋅e
Mirco Ravanelli
LinkedIn
GitHub

Publications

GLaS-JEPA: Gaussian-Regularized Speech SSL without Engineered Prediction Targets
Gaspard Botté
Séverin Baroudi
Samir Sadok
Francesco Paissan
Thomas Hueber
Xavier Alameda-Pineda
Ricard Marxer
Mirco Ravanelli
Speech self-supervised learning aims to learn general-purpose representations for downstream speech tasks. However, current approaches rely … (voir plus)on complex, carefully designed prediction targets. We challenge this necessity with GLaS-JEPA, a framework that directly predicts the current encoder's continuous representations at masked positions, without contrastive learning, discrete targets, or separate EMA target encoders. We prevent representation collapse using SIGReg representation-space regularization, eliminating the need for engineered target-generation mechanisms. Pretrained on 960 hours of LibriSpeech, our 57M-parameter model achieves a 6.89% WER on frozen-encoder SUPERB ASR and a 25.87% CER on slot filling, outperforming the best non-distilled sub-90M baselines by 43.1% and 22.0%, respectively. These results demonstrate that highly competitive speech representations can emerge from a radically simplified training recipe.
2026-09-28
arXiv (prépublication)
doi.org
arxiv.org