45 citations · 211 across the 23 of their papers we have counts for
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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…
Scaling Properties of Speech Language Models
Santiago Cuervo, Ricard Marxer
Speech Language Models (SLMs) aim to learn language from raw audio, without textual resources. Despite significant advances, our current models exhibit weak syntax and semantic abi…
Transfer Learning from Whisper for Microscopic Intelligibility Prediction
Paul Best, Santiago Cuervo, Ricard Marxer
Macroscopic intelligibility models predict the expected human word-error-rate for a given speech-in-noise stimulus. In contrast, microscopic intelligibility models aim to make fine…
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…
Speech foundation models on intelligibility prediction for hearing-impaired listeners
Santiago Cuervo, Ricard Marxer
Speech foundation models (SFMs) have been benchmarked on many speech processing tasks, often achieving state-of-the-art performance with minimal adaptation. However, the SFM paradi…