Combining speakers of multiple languages to improve quality of neural voices

• URL: http://arxiv.org/abs/2108.07737v1
• Date: Tue, 17 Aug 2021 16:14:13 GMT
• Title: Combining speakers of multiple languages to improve quality of neural voices
• Authors: Javier Latorre, Charlotte Bailleul, Tuuli Morrill, Alistair Conkie, Yannis Stylianou
• Abstract summary: We report results from a large experiment using 30 speakers in 8 different languages across 15 different locales. In cross-lingual synthesis, on average, the generated quality is within $80%$ of native single-speaker models, in terms of Mean Opinion Score.
• Score: 21.417373542428617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 single-speaker model. In cross-lingual synthesis, on average, the generated quality is within $80\%$ of native single-speaker models, in terms of Mean Opinion Score.

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