most citedEnhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations

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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…

eess.AS2024★ 1 cited

Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations

Álvaro Martín-Cortinas, Daniel Sáez-Trigueros, Iván Vallés-Pérez +6

Large Language Models (LLMs) are one of the most promising technologies for the next era of speech generation systems, due to their scalability and in-context learning capabilities…