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 build low-resource, neural text-to-speech (TTS) voices with only 1 hour of conversational speech, when no other conversational data are available in the same language. Assuming the availability of non-expressive speech data in that language, we propose a 3-step technology: 1) we train an F0-conditioned voice conversion (VC) model as data augmentation technique; 2) we train an F0 predictor to control the conversational flavour of the voice-converted synthetic data; 3) we train a TTS system that consumes the augmented data. We prove that our technology enables F0 controllability, is scalable across speakers and languages and is competitive in terms of naturalness over a state-of-the-art baseline model, another augmented method which does not make use of F0 information.
@article{arxiv.2207.14607,
title = {Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation},
author = {Giulia Comini and Goeric Huybrechts and Manuel Sam Ribeiro and Adam Gabrys and Jaime Lorenzo-Trueba},
journal= {arXiv preprint arXiv:2207.14607},
year = {2022}
}