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

• URL: http://arxiv.org/abs/2511.06288v1
• Date: Sun, 09 Nov 2025 08:50:11 GMT
• Title: ELEGANCE: Efficient LLM Guidance for Audio-Visual Target Speech Extraction
• Authors: Wenxuan Wu, Shuai Wang, Xixin Wu, Helen Meng, Haizhou Li,
• Abstract summary: We propose ELEGANCE, a novel framework that incorporates linguistic knowledge from large language models (LLMs) into AV-TSE models.<n> Comprehensive experiments with RoBERTa, Qwen3-0.6B, and Qwen3-4B on two AV-TSE backbones show significant improvements.
• Score: 88.41471266579333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 demon- strate 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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