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Context-aware Coherent Speaking Style Prediction with Hierarchical Transformers for Audiobook Speech Synthesis by Lei, Shun; Zhou, Yixuan; Chen, Liyang; Wu, Zhiyong; Kang, Shiyin; Meng, Helen is a scholarly article available to read on EtoBox.

What is Context-aware Coherent Speaking Style Prediction with Hierarchical Transformers for Audiobook Speech Synthesis about?

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.

Author

Lei, Shun; Zhou, Yixuan; Chen, Liyang; Wu, Zhiyong; Kang, Shiyin; Meng, Helen

Published

2023

Language

EN