activity
20202024
most citedCross-speaker style transfer for text-to-speech using data augmentation

2 citations · 3 across the 6 of their papers we have counts for

collaborators

7 papers

eess.AS2024

Del Visual al Auditivo: Sonorización de Escenas Guiada por Imagen

María Sánchez, Laura Fernández, Julián Arias +6

Recent advances in image, video, text and audio generative techniques, and their use by the general public, are leading to new forms of content generation. Usually, each modality w…

cs.CL2023

Multilingual context-based pronunciation learning for Text-to-Speech

Giulia Comini, Manuel Sam Ribeiro, Fan Yang +2

Phonetic information and linguistic knowledge are an essential component of a Text-to-speech (TTS) front-end. Given a language, a lexicon can be collected offline and Grapheme-to-P…

eess.AS2023

Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings

Manuel Sam Ribeiro, Giulia Comini, Jaime Lorenzo-Trueba

The Grapheme-to-Phoneme (G2P) task aims to convert orthographic input into a discrete phonetic representation. G2P conversion is beneficial to various speech processing application…

eess.AS2022★ 1 cited

Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation

Giulia Comini, Goeric Huybrechts, Manuel Sam Ribeiro +2

The availability of data in expressive styles across languages is limited, and recording sessions are costly and time consuming. To overcome these issues, we demonstrate how to bui…

eess.AS2022

Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module

Adam Gabryś, Goeric Huybrechts, Manuel Sam Ribeiro +6

State-of-the-art text-to-speech (TTS) systems require several hours of recorded speech data to generate high-quality synthetic speech. When using reduced amounts of training data,…

eess.AS2022★ 2 cited

Cross-speaker style transfer for text-to-speech using data augmentation

Manuel Sam Ribeiro, Julian Roth, Giulia Comini +3

We address the problem of cross-speaker style transfer for text-to-speech (TTS) using data augmentation via voice conversion. We assume to have a corpus of neutral non-expressive d…

eess.AS2020

Low-resource expressive text-to-speech using data augmentation

Goeric Huybrechts, Thomas Merritt, Giulia Comini +3

While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired s…