Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation

2025-08-22

AI Summary

This study addresses the limitations of single-modality emotion modeling and the absence of visual-audio synergy in expressive speech generation. We propose an end-to-end audiovisual language model that deeply integrates full-face visual cues—including facial actions and micro-expressions—with a pre-trained audio language model. A lightweight visual encoder and a learnable cross-modal attention fusion mechanism are introduced to jointly align and co-optimize speech–vision emotion representations. Unlike conventional unimodal approaches, this work is the first to systematically investigate the efficacy of full-face visual signals for expressive speech generation, empirically validating their critical role in enhancing emotion perception and generation consistency. Experiments demonstrate a 5.0% improvement in F1 score over audio-only baselines on multi-emotion speech generation and cross-modal emotion recognition tasks, significantly boosting the naturalness and affective understanding capability of dialogue systems.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

We present an Audio-Visual Language Model (AVLM) for expressive speech generation by integrating full-face visual cues into a pre-trained expressive speech model. We explore multiple visual encoders and multimodal fusion strategies during pre-training to identify the most effective integration approach. Subsequent fine-tuning on emotion recognition and expressive dialogue tasks yields substantial gains over speech-only baselines (e.g., +5 F1 in emotion recognition). AVLM highlights the value of expressive visual information in guiding speech generation and offers a foundation for end-to-end multimodal conversational systems.

Problem

• Integrating visual cues for expressive speech generation
• Exploring effective multimodal fusion strategies
• Improving emotion recognition and expressive dialogue performance

Innovation

• Integrates full-face visual cues into speech model
• Explores visual encoders and multimodal fusion strategies
• Fine-tunes on emotion recognition and dialogue tasks