ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion

Autor:	Casanova, Edresson, Shulby, Christopher, Korolev, Alexander, Junior, Arnaldo Candido, Soares, Anderson da Silva, Aluísio, Sandra, Ponti, Moacir Antonelli
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Computation and Language Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	We explore cross-lingual multi-speaker speech synthesis and cross-lingual voice conversion applied to data augmentation for automatic speech recognition (ASR) systems in low/medium-resource scenarios. Through extensive experiments, we show that our approach permits the application of speech synthesis and voice conversion to improve ASR systems using only one target-language speaker during model training. We also managed to close the gap between ASR models trained with synthesized versus human speech compared to other works that use many speakers. Finally, we show that it is possible to obtain promising ASR training results with our data augmentation method using only a single real speaker in a target language. Comment: This paper was accepted at INTERSPEECH 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.00618 Zobrazit plný text záznamu View this record from Arxiv