• Corpus ID: 238857242

Improve Cross-lingual Voice Cloning Using Low-quality Code-switched Data

@article{Zhang2021ImproveCV,
  title={Improve Cross-lingual Voice Cloning Using Low-quality Code-switched Data},
  author={Haitong Zhang and Yue Lin},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.07210}
}
Recently, sequence-to-sequence (seq-to-seq) models have been successfully applied in text-to-speech (TTS) to synthesize speech for single-language text. To synthesize speech for multiple languages usually requires multi-lingual speech from the target speaker. However, it is both laborious and expensive to collect high-quality multi-lingual TTS data for the target speakers. In this paper, we proposed to use low-quality code-switched found data from the non-target speakers to achieve cross… 

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References

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TLDR
This work proposes a framework to synthesize code-mixed text by using a TTS database in a single language, identifying the language that each word was from, normalizing spellings of a language written in a non-standardized script and mapping the phonetic space of mixed language to thelanguage that the T TS database was recorded in.
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