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arXiv · 2305.02269

M2-CTTS: End-to-End Multi-scale Multi-modal Conversational Text-to-Speech Synthesis

Jinlong Xue·Yayue Deng·Fengping Wang·Ya Li·Yingming Gao·Jianhua Tao·Jianqing Sun·Jiaen Liang
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Abstract

Conversational text-to-speech (TTS) aims to synthesize speech with proper prosody of reply based on the historical conversation. However, it is still a challenge to comprehensively model the conversation, and a majority of conversational TTS systems only focus on extracting global information and omit local prosody features, which contain important fine-grained information like keywords and emphasis. Moreover, it is insufficient to only consider the textual features, and acoustic features also contain various prosody information. Hence, we propose M2-CTTS, an end-to-end multi-scale multi-modal conversational text-to-speech system, aiming to comprehensively utilize historical conversation and enhance prosodic expression. More specifically, we design a textual context module and an acoustic context module with both coarse-grained and fine-grained modeling. Experimental results demonstrate that our model mixed with fine-grained context information and additionally considering acoustic features achieves better prosody performance and naturalness in CMOS tests.

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Jinlong Xue, Yayue Deng, Fengping Wang, Ya Li, Yingming Gao, Jianhua Tao, Jianqing Sun, Jiaen Liang. 2023-05-03. M2-CTTS: End-to-End Multi-scale Multi-modal Conversational Text-to-Speech Synthesis. https://arxiv.org/abs/2305.02269

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