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arXiv · 2511.06288

ELEGANCE: Efficient LLM Guidance for Audio-Visual Target Speech Extraction

Wenxuan Wu·Shuai Wang·Xixin Wu·Helen Meng·Haizhou Li
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Abstract

Audio-visual target speaker extraction (AV-TSE) models primarily rely on visual cues from the target speaker. However, humans also leverage linguistic knowledge, such as syntactic constraints, next word prediction, and prior knowledge of conversation, to extract target speech. Inspired by this observation, we propose ELEGANCE, a novel framework that incorporates linguistic knowledge from large language models (LLMs) into AV-TSE models through three distinct guidance strategies: output linguistic constraints, intermediate linguistic prediction, and input linguistic prior. Comprehensive experiments with RoBERTa, Qwen3-0.6B, and Qwen3-4B on two AV-TSE backbones demonstrate the effectiveness of our approach. Significant improvements are observed in challenging scenarios, including visual cue impaired, unseen languages, target speaker switches, increased interfering speakers, and out-of-domain test set. Demo page: https://alexwxwu.github.io/ELEGANCE/.

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Wenxuan Wu, Shuai Wang, Xixin Wu, Helen Meng, Haizhou Li. 2025-11-09. ELEGANCE: Efficient LLM Guidance for Audio-Visual Target Speech Extraction. https://arxiv.org/abs/2511.06288

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