Author

Séverin Baroudi

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#machine learning Preprint Oct 2026

SepRQ : Self-Supervised Speech Mixture Representation Learning via Mask-Free, Multi-Scale Source Separation

Self-supervised learning (SSL) is standard for speech representation learning, but mainstream models are designed around single-speaker audio, limiting their usefulness in multi-speakers scenarios. We present SepRQ, an open-source SSL framework that replaces masked prediction with a pseudo-source-separation objective o...

Séverin Baroudi, Hervé Bredin, R. Marxer · 0 citations

#artificial intelligence Preprint Sep 2026

GLaS-JEPA: Gaussian-Regularized Speech SSL without Engineered Prediction Targets

Speech self-supervised learning aims to learn general-purpose representations for downstream speech tasks. However, current approaches rely on complex, carefully designed prediction targets. We challenge this necessity with GLaS-JEPA, a framework that directly predicts the current encoder's continuous representations a...

G. Botté, Séverin Baroudi, Samir Sadok et al. · 0 citations