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• W3195537094 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." @default.
• W3195537094 created "2021-08-30" @default.
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• W3195537094 date "2021-08-17" @default.
• W3195537094 modified "2023-09-27" @default.
• W3195537094 title "Combining speakers of multiple languages to improve quality of neural voices" @default.
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