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eess.AS2025★ 1 cited
Universal Semantic Disentangled Privacy-preserving Speech Representation Learning
Biel Tura Vecino, Subhadeep Maji, Aravind Varier +11
The use of audio recordings of human speech to train LLMs poses privacy concerns due to these models' potential to generate outputs that closely resemble artifacts in the training…
eess.AS2025
Investigating self-supervised features for expressive, multilingual voice conversion
Álvaro Martín-Cortinas, Daniel Sáez-Trigueros, Grzegorz Beringer +7
Voice conversion (VC) systems are widely used for several applications, from speaker anonymisation to personalised speech synthesis. Supervised approaches learn a mapping between d…