# Multimodal Fine-grained Context Interaction Graph Modeling for Conversational Speech Synthesis

arXiv: https://arxiv.org/abs/2509.06074 · HTML: https://mlsanity.com/paper/2509.06074/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 7 Sep 2025.

## Builds on (most cited first, 19 of 19)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://mlsanity.com/paper/2004.06871/) (2020)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech](https://mlsanity.com/paper/2207.01063/) (2022)
- [Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations](https://mlsanity.com/paper/2402.12786/) (2024)
- [Conversational End-to-End TTS for Voice Agent](https://mlsanity.com/paper/2005.10438/) (2020)
- [IMF: Interactive Multimodal Fusion Model for Link Prediction](https://mlsanity.com/paper/2303.10816/) (2023)
- [Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling](https://mlsanity.com/paper/2312.11947/) (2023)
- [Enhancing Speaking Styles in Conversational Text-to-Speech Synthesis with Graph-based Multi-modal Context Modeling](https://mlsanity.com/paper/2106.06233/) (2021)
- [MM-TTS: Multi-modal Prompt based Style Transfer for Expressive Text-to-Speech Synthesis](https://mlsanity.com/paper/2312.10687/) (2023)
- [FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis](https://mlsanity.com/paper/2210.15360/) (2022)
- [CONCSS: Contrastive-based Context Comprehension for Dialogue-appropriate Prosody in Conversational Speech Synthesis](https://mlsanity.com/paper/2312.10358/) (2023)
- [Emphasis Rendering for Conversational Text-to-Speech with Multi-modal Multi-scale Context Modeling](https://mlsanity.com/paper/2410.09524/) (2024)
- [Intra- and Inter-modal Context Interaction Modeling for Conversational Speech Synthesis](https://mlsanity.com/paper/2412.18733/) (2024)
- [Modeling Intention, Emotion and External World in Dialogue Systems](https://mlsanity.com/paper/2202.06476/) (2022)
- [Retrieval-Augmented Dialogue Knowledge Aggregation for Expressive Conversational Speech Synthesis](https://mlsanity.com/paper/2501.06467/) (2025)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Conversational Speech Synthesis (CSS) aims to generate speech with natural prosody by understanding the multimodal dialogue history (MDH). The latest work predicts the accurate prosody expression of the target utterance by modeling the utterance-level interaction characteristics of MDH and the target utterance. However, MDH contains fine-grained semantic and prosody knowledge at the word level. Existing methods overlook the fine-grained semantic and prosodic interaction modeling. To address this gap, we propose MFCIG-CSS, a novel Multimodal Fine-grained Context Interaction Graph-based CSS system. Our approach constructs two specialized multimodal fine-grained dialogue interaction graphs: a semantic interaction graph and a prosody interaction graph. These two interaction graphs effectively encode interactions between word-level semantics, prosody, and their influence on subsequent utterances in MDH. The encoded interaction features are then leveraged to enhance synthesized speech with natural conversational prosody. Experiments on the DailyTalk dataset demonstrate that MFCIG-CSS outperforms all baseline models in terms of prosodic expressiveness. Code and speech samples are available at https://github.com/AI-S2-Lab/MFCIG-CSS.