Cross-Dialect Text-To-Speech in Pitch-Accent Language Incorporating Multi-Dialect Phoneme-Level BERT

• URL: http://arxiv.org/abs/2409.07265v1
• Date: Wed, 11 Sep 2024 13:40:27 GMT
• Title: Cross-Dialect Text-To-Speech in Pitch-Accent Language Incorporating Multi-Dialect Phoneme-Level BERT
• Authors: Kazuki Yamauchi, Yuki Saito, Hiroshi Saruwatari,
• Abstract summary: Cross-dialect text-to-speech (CD-TTS) is a task to synthesize learned speakers' voices in non-native dialects. We present a novel TTS model comprising three sub-modules to perform competitively at this task.
• Score: 29.167336994990542
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We explore cross-dialect text-to-speech (CD-TTS), a task to synthesize learned speakers' voices in non-native dialects, especially in pitch-accent languages. CD-TTS is important for developing voice agents that naturally communicate with people across regions. We present a novel TTS model comprising three sub-modules to perform competitively at this task. We first train a backbone TTS model to synthesize dialect speech from a text conditioned on phoneme-level accent latent variables (ALVs) extracted from speech by a reference encoder. Then, we train an ALV predictor to predict ALVs tailored to a target dialect from input text leveraging our novel multi-dialect phoneme-level BERT. We conduct multi-dialect TTS experiments and evaluate the effectiveness of our model by comparing it with a baseline derived from conventional dialect TTS methods. The results show that our model improves the dialectal naturalness of synthetic speech in CD-TTS.

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