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arXiv · 2005.10438

Conversational End-to-End TTS for Voice Agent

Haohan Guo·Shaofei Zhang·Frank K. Soong·Lei He·Lei Xie
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Abstract

End-to-end neural TTS has achieved superior performance on reading style speech synthesis. However, it's still a challenge to build a high-quality conversational TTS due to the limitations of the corpus and modeling capability. This study aims at building a conversational TTS for a voice agent under sequence to sequence modeling framework. We firstly construct a spontaneous conversational speech corpus well designed for the voice agent with a new recording scheme ensuring both recording quality and conversational speaking style. Secondly, we propose a conversation context-aware end-to-end TTS approach which has an auxiliary encoder and a conversational context encoder to reinforce the information about the current utterance and its context in a conversation as well. Experimental results show that the proposed methods produce more natural prosody in accordance with the conversational context, with significant preference gains at both utterance-level and conversation-level. Moreover, we find that the model has the ability to express some spontaneous behaviors, like fillers and repeated words, which makes the conversational speaking style more realistic.

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Haohan Guo, Shaofei Zhang, Frank K. Soong, Lei He, Lei Xie. 2020-05-21. Conversational End-to-End TTS for Voice Agent. https://arxiv.org/abs/2005.10438

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