Can Speaker Augmentation Improve Multi-Speaker End-to-End TTS?

Authors: Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Junichi Yamagishi

Accepted to Interspeech 2020.

This is an extension of our previous work, "Zero-Shot Multi-Speaker Text-to-Speech with State-of-the-Art Neural Speaker Embeddings" (ICASSP 2020). We try a number of different approaches for speaker space augmentation to improve speaker similarity. The first approach is artificial speaker augmentation, by speeding up and slowing down the existing VCTK data to create artificial "speakers". The next one is to include lower-quality ASR data to increase the number of speakers seen during training. We also experiment with adding channel labels, to counteract the lower quality of the data in the ASR corpora, as well as adding dialect embeddings to better model speakers' accents.

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