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

Yifan Hu, Rui Liu, Yi Ren, Xiang Yin, Haizhou Li

TL;DR

Chain-Talker tackles the interpretability and empathy gap in conversational speech synthesis by decomposing CSS into three stages—Emotion Understanding, Semantic Understanding, and Empathetic Rendering—controlled through a unified context tokenization pipeline. It introduces CSS-EmCap, an LLM-driven pipeline that annotates dialog-aware empathetic captions to train the model on expressive, context-relevant speech. Across three benchmark datasets, Chain-Talker outperforms strong baselines on objective metrics like DDTW and SSIM and achieves higher subjective scores for naturalness and expressiveness, while CSS-EmCap enables reliable emotion modeling and diverse caption generation. The work advances interpretable, controllable CSS and provides resources for community adoption, with considerations for latency and demographic coverage in future improvements.

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.

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

TL;DR

Chain-Talker tackles the interpretability and empathy gap in conversational speech synthesis by decomposing CSS into three stages—Emotion Understanding, Semantic Understanding, and Empathetic Rendering—controlled through a unified context tokenization pipeline. It introduces CSS-EmCap, an LLM-driven pipeline that annotates dialog-aware empathetic captions to train the model on expressive, context-relevant speech. Across three benchmark datasets, Chain-Talker outperforms strong baselines on objective metrics like DDTW and SSIM and achieves higher subjective scores for naturalness and expressiveness, while CSS-EmCap enables reliable emotion modeling and diverse caption generation. The work advances interpretable, controllable CSS and provides resources for community adoption, with considerations for latency and demographic coverage in future improvements.

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.
Paper Structure (36 sections, 5 equations, 5 figures, 6 tables)

This paper contains 36 sections, 5 equations, 5 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Chain Modeling in Conversation
  4. Speech Emotion Description
  5. Speech Discrete Encoding
  6. Task Definition
  7. Methodlogy: Chain-Talker
  8. Unified Context Tokenization
  9. Emotion Understanding
  10. Semantic Understanding
  11. Empathetic Rendering
  12. Multi-Stage Training
  13. CSS-EmCap Pipeline
  14. Multi-level Attribute Extraction
  15. Empathetic Captions Generation
  16. ...and 21 more sections

Figures (5)

  • Figure 1: (a) Previous methods predict speech tokens directly based on context. (b) Our approach progressively realizes empathetic CSS through three stages: Emotion Understanding, Semantic Understanding, and Empathetic Rendering.
  • Figure 2: The overall architecture of Chain-Talker. Chain-Talker comprises two main components: EmGPT and Synthesizer. EmGPT is responsible for emotion and semantic understanding, while the Synthesizer handles the generation of empathetic speech rendering.
  • Figure 3: (a) The ability of Chain-Talker and various baseline models to synthesize target speech with different emotional categories based on dialogue context. (b) Experimental results on the selection of the hyperparameter $N$ for dialogue turns.
  • Figure 4: The overall process of CSS-EmCap, It includes extracting Sentence-level style factors and Dialog-level emoton, as well as prompting LLM to generate Basic Descriptions and final Empathetic Captions.
  • Figure 5: A sample set of conversational data annotated using the CSS-EmCap pipeline.