August 19, 2023
Recent work has shown that it is possible to resynthesize high-quality speech based, not on text, but on low bitrate discrete units that have been learned in a self-supervised fashion and can therefore capture expressive aspects of speech that are hard to transcribe (prosody, voice styles, non-verbal vocalization). The adoption of these methods is still limited by the fact that most speech synthesis datasets are read, severely limiting spontaneity and expressivity. Here, we introduce \expresso, a high-quality expressive speech dataset for textless speech synthesis that includes both read speech and improvised dialogues rendered in 26 spontaneous expressive styles. We illustrate the challenges and potentials of this dataset with an \textit{expressive resynthesis benchmark} where the task is to encode the input in low-bitrate units and resynthesize it in a target voice while preserving content and style. We evaluate resynthesis quality with automatic metrics for different self-supervised discrete encoders, and explore tradeoffs between quality, bitrate and invariance to speaker and style. The dataset, evaluation metrics and baseline models will be open sourced.
Written by
Tu Anh Nguyen
Antony D'Avirro
Bowen Shi
Tal Remez
Jade Copet
Michael Hassid
Felix Kreuk
Emmanuel Dupoux
Publisher
Interspeech
Research Topics
February 07, 2025
The Omnilingual MT Team, Pierre Andrews, Mikel Artetxe, Mariano Coria Meglioli, Marta R. Costa-jussa, Joe Chuang, David Dale, Cynthia Gao, Jean Maillard, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Eduardo Sánchez, Yiannis Tsiamas, Arina Turkatenko, Albert Ventayol, Shireen Yates
February 07, 2025
February 06, 2025
Jarod Levy, Mingfang (Lucy) Zhang, Svetlana Pinet, Jérémy Rapin, Hubert Jacob Banville, Stéphane d'Ascoli, Jean Remi King
February 06, 2025
February 06, 2025
Mingfang (Lucy) Zhang, Jarod Levy, Stéphane d'Ascoli, Jérémy Rapin, F.-Xavier Alario, Pierre Bourdillon, Svetlana Pinet, Jean Remi King
February 06, 2025
January 04, 2025
January 04, 2025
Foundational models
Our approach
Latest news
Foundational models