LLM guides reinforcement learning for enhanced audio-visual speech

Researchers have developed a novel audio-visual speech enhancement (AVSE) system that utilizes a Large Language Model (LLM) to guide the reinforcement learning process. Instead of relying solely on traditional metrics like SI-SNR, this method employs an LLM to generate natural language descriptions of speech quality, which are then translated into a reward signal for fine-tuning the AVSE model. Experiments on the AVSEC-4 dataset demonstrated that this LLM-guided approach surpasses supervised baselines and a DNSMOS-based RL baseline in various objective and subjective listening tests. AI

IMPACT This LLM-guided approach could lead to more nuanced and human-like speech enhancement systems by leveraging semantic understanding.

RANK_REASON Academic paper detailing a novel method for audio-visual speech enhancement. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Audio-Visual Speech Enhancement
• AVSEC-4 dataset
• DNSMOS
• Large Language Model
• Proximal Policy Optimization
• Reinforcement Learning
• SI-SNR
• Yu Tsao

• paper
• other

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