Combining speakers of multiple languages to improve quality of neural voices
Javier Latorre, Charlotte Bailleul, Tuuli H. Morrill, Alistair D. Conkie, Yannis Stylianou · 2021
In this work, we explore multiple architectures and training procedures for developing a multi-speaker and multi-lingual neural TTS system with the goals of a) improving the quality when the available data in the target language is limited and b) enabling cross-lingual synthesis.We report results from a large experiment using 30 speakers in 8 different languages across 15 different locales.The system is trained on the same amount of data per speaker.Compared to a single-speaker model, when the suggested system is fine tuned to a speaker, it produces significantly better quality in most of the cases while it only uses less than 40% of the speaker's data used to build the singlespeaker model.In cross-lingual synthesis, on average, the generated quality is within 80% of native single-speaker models, in terms of Mean Opinion Score.