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arXiv · 2505.12597

Chain-Talker: Chain Understanding and Rendering for Empathetic Conversational Speech Synthesis

Yifan Hu·Rui Liu·Yi Ren·Xiang Yin·Haizhou Li
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Abstract

Conversational Speech Synthesis (CSS) aims to align synthesized speech with the emotional and stylistic context of user-agent interactions to achieve empathy. Current generative CSS models face interpretability limitations due to insufficient emotional perception and redundant discrete speech coding. To address the above issues, we present Chain-Talker, a three-stage framework mimicking human cognition: Emotion Understanding derives context-aware emotion descriptors from dialogue history; Semantic Understanding generates compact semantic codes via serialized prediction; and Empathetic Rendering synthesizes expressive speech by integrating both components. To support emotion modeling, we develop CSS-EmCap, an LLM-driven automated pipeline for generating precise conversational speech emotion captions. Experiments on three benchmark datasets demonstrate that Chain-Talker produces more expressive and empathetic speech than existing methods, with CSS-EmCap contributing to reliable emotion modeling. The code and demos are available at: https://github.com/AI-S2-Lab/Chain-Talker.

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Yifan Hu, Rui Liu, Yi Ren, Xiang Yin, Haizhou Li. 2025-05-19. Chain-Talker: Chain Understanding and Rendering for Empathetic Conversational Speech Synthesis. https://arxiv.org/abs/2505.12597

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