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

Autor(es)

Casanova, Edresson ; Shulby, Christopher ; Korolev, Alexander ; Candido Junior, Arnaldo ; Soares, Anderson da Silva ; Aluisio, Sandra ; Ponti, Moacir Antonelli

• Tipo de documento: Artigo Científico
• Fonte: 18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6; v. N/A, p. 5-pg., 2023-01-01.

Resumo

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. (AU)

• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia