Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement

• URL: http://arxiv.org/abs/2011.06392v2
• Date: Thu, 31 Mar 2022 15:49:56 GMT
• Title: Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement
• Authors: Hamed Hemati, Damian Borth
• Abstract summary: We show that one can transfer an existing TTS model for new speakers from the same or a different language using only 20 minutes of data. We first introduce a base multi-lingual Tacotron with language-agnostic input, then demonstrate how transfer learning is done for different scenarios of speaker adaptation.
• Score: 1.7704011486040843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent neural Text-to-Speech (TTS) models have been shown to perform very well when enough data is available. However, fine-tuning them for new speakers or languages is not straightforward in a low-resource setup. In this paper, we show that by applying minor modifications to a Tacotron model, one can transfer an existing TTS model for new speakers from the same or a different language using only 20 minutes of data. For this purpose, we first introduce a base multi-lingual Tacotron with language-agnostic input, then demonstrate how transfer learning is done for different scenarios of speaker adaptation without exploiting any pre-trained speaker encoder or code-switching technique. We evaluate the transferred model in both subjective and objective ways.

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