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

arXiv: https://arxiv.org/abs/2505.12597 · HTML: https://mlsanity.com/paper/2505.12597/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2025.

## Built on by (most cited first, 7 of 7)

- [EmoShift: Lightweight Activation Steering for Enhanced Emotion-Aware Speech Synthesis](https://mlsanity.com/paper/2601.22873/) (2026)
- [AuEmoChat: Authentic Emotion Understanding and Rendering for Conversational Speech Synthesis](https://mlsanity.com/paper/2607.15755/) (2026)
- [ES4R: Speech Encoding Based on Prepositive Affective Modeling for Empathetic Response Generation](https://mlsanity.com/paper/2601.16225/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [S2Dialog: Multimodal Dialogue Retrieval with Semantic and Acoustic-Style Modeling](https://mlsanity.com/paper/2608.14029/) (2026)
- [Listen Before You Speak: Response Planning from Listener Facial Reactions for Conversational Speech Generation](https://mlsanity.com/paper/2609.21683/) (2026)
- [COT-TTS: Audio Context-Aware Text-to-Speech with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2609.22697/) (2026)

## Abstract (arXiv)

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.