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arXiv · 2304.06359

Context-aware Coherent Speaking Style Prediction with Hierarchical Transformers for Audiobook Speech Synthesis

Shun Lei·Yixuan Zhou·Liyang Chen·Zhiyong Wu·Shiyin Kang·Helen Meng
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Abstract

Recent advances in text-to-speech have significantly improved the expressiveness of synthesized speech. However, it is still challenging to generate speech with contextually appropriate and coherent speaking style for multi-sentence text in audiobooks. In this paper, we propose a context-aware coherent speaking style prediction method for audiobook speech synthesis. To predict the style embedding of the current utterance, a hierarchical transformer-based context-aware style predictor with a mixture attention mask is designed, considering both text-side context information and speech-side style information of previous speeches. Based on this, we can generate long-form speech with coherent style and prosody sentence by sentence. Objective and subjective evaluations on a Mandarin audiobook dataset demonstrate that our proposed model can generate speech with more expressive and coherent speaking style than baselines, for both single-sentence and multi-sentence test.

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Shun Lei, Yixuan Zhou, Liyang Chen, Zhiyong Wu, Shiyin Kang, Helen Meng. 2023-04-13. Context-aware Coherent Speaking Style Prediction with Hierarchical Transformers for Audiobook Speech Synthesis. https://arxiv.org/abs/2304.06359

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