15 citations · 15 across the 3 of their papers we have counts for
4 papers
SepRQ : Self-Supervised Speech Mixture Representation Learning via Mask-Free, Multi-Scale Source Separation
Séverin Baroudi, Hervé Bredin, Ricard Marxer
Self-supervised learning (SSL) is standard for speech representation learning, but mainstream models are designed around single-speaker audio, limiting their usefulness in multi-sp…
On the Use of Self-Supervised Representation Learning for Speaker Diarization and Separation
Séverin Baroudi, Hervé Bredin, Joseph Razik +1
Self-supervised speech models such as wav2vec2.0 and WavLM have been shown to significantly improve the performance of many downstream speech tasks, especially in low-resource sett…
TalTech-IRIT-LIS Speaker and Language Diarization Systems for DISPLACE 2024
Joonas Kalda, Tanel Alumäe, Martin Lebourdais +3
This paper describes the submissions of team TalTech-IRIT-LIS to the DISPLACE 2024 challenge. Our team participated in the speaker diarization and language diarization tracks of th…
PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker Recordings
Joonas Kalda, Clément Pagés, Ricard Marxer +2
A major drawback of supervised speech separation (SSep) systems is their reliance on synthetic data, leading to poor real-world generalization. Mixture invariant training (MixIT) w…