Efficient neural speech synthesis for low-resource languages through multilingual modeling

Marcel de Korte; Jaebok Kim; Esther Klabbers

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arXiv · 2008.09659

Efficient neural speech synthesis for low-resource languages through multilingual modeling

Marcel de Korte·Jaebok Kim·Esther Klabbers

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Abstract

Recent advances in neural TTS have led to models that can produce high-quality synthetic speech. However, these models typically require large amounts of training data, which can make it costly to produce a new voice with the desired quality. Although multi-speaker modeling can reduce the data requirements necessary for a new voice, this approach is usually not viable for many low-resource languages for which abundant multi-speaker data is not available. In this paper, we therefore investigated to what extent multilingual multi-speaker modeling can be an alternative to monolingual multi-speaker modeling, and explored how data from foreign languages may best be combined with low-resource language data. We found that multilingual modeling can increase the naturalness of low-resource language speech, showed that multilingual models can produce speech with a naturalness comparable to monolingual multi-speaker models, and saw that the target language naturalness was affected by the strategy used to add foreign language data.

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BibTeX RIS

Marcel de Korte, Jaebok Kim, Esther Klabbers. 2020-08-20. Efficient neural speech synthesis for low-resource languages through multilingual modeling. https://arxiv.org/abs/2008.09659

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