LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement
Abstract
In existing Audio-Visual Speech Enhancement (AVSE) methods, objectives such as Scale-Invariant Signal-to-Noise Ratio (SI-SNR) and Mean Squared Error (MSE) are widely used; however, they often correlate poorly with perceptual quality and provide limited interpretability for optimization. This work proposes a reinforcement learning-based AVSE framework with a Large Language Model (LLM)-based interpretable reward model. An audio LLM generates natural language descriptions of enhanced speech, which are converted by a sentiment analysis model into a 1-5 rating score serving as the PPO reward for fine-tuning a pretrained AVSE model. Compared with scalar metrics, LLM-generated feedback is semantically rich and explicitly describes improvements in speech quality. Experiments on the 4th COG-MHEAR AVSE Challenge (AVSEC-4) dataset show that the proposed method outperforms a supervised baseline and a DNSMOS-based RL baseline in PESQ, STOI, neural quality metrics, and subjective listening tests.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of an LLM-guided reinforcement learning framework for audio-visual speech enhancement, which enhances interpretability and aligns model training with human perceptual quality assessments. This work represents a significant advancement in the field, combining innovative methodologies with rigorous experimental validation to address longstanding challenges in speech enhancement.
Methodology
The proposed methodology introduces a novel reinforcement learning framework that leverages Large Language Models (LLMs) to generate interpretable reward signals for audio-visual speech enhancement. The integration of natural language descriptions as rewards represents a significant departure from traditional scalar metrics, enhancing the interpretability and alignment of the training process with human perception. The use of sentiment analysis to convert these descriptions into numerical scores for reinforcement learning is innovative and adds depth to the reward model design. However, the methodology could benefit from a more detailed exploration of the LLM's limitations and the potential for more advanced models to enhance the reward generation process.
Results & evaluation
The experimental evaluation is robust, utilizing the 4th COG-MHEAR AVSE Challenge dataset, which provides a comprehensive benchmark for assessing the proposed method's performance. The results demonstrate significant improvements over both a supervised baseline and a DNSMOS-based RL baseline across multiple objective and subjective metrics, including PESQ and STOI. The subjective listening tests further validate the effectiveness of the LLM-based reward model, showcasing its ability to enhance perceived speech quality. The experiments are well-structured, but additional comparisons with other state-of-the-art methods could strengthen the claims of superiority.
Reproducibility
The paper provides a clear description of the methodology, including the architecture of the AVSE model and the reinforcement learning framework. However, the lack of publicly available code or a demo URL limits reproducibility. Detailed hyperparameter settings and training procedures are mentioned, but sharing the model weights and training scripts would facilitate independent verification of the results.
Limitations
The primary limitation identified is the reliance on a specific LLM (SALMONN) for generating reward signals, which may not generalize well across different speech enhancement tasks or datasets. Additionally, the repetitive nature of the generated natural language descriptions could hinder the model's ability to capture subtle differences in speech quality. Future work should address these limitations by exploring more advanced LLMs and refining prompt engineering strategies.
Broader impact
The proposed framework has the potential to significantly impact the field of audio-visual speech enhancement by providing a more interpretable and human-aligned approach to model training. The integration of LLMs into the reinforcement learning paradigm could pave the way for advancements in other multimodal applications, enhancing the interpretability and effectiveness of AI systems in various domains, including assistive technologies for hearing-impaired individuals. The main contribution of this paper is the introduction of an LLM-guided reinforcement learning framework for audio-visual speech enhancement, which enhances interpretability and aligns model training with human perceptual quality assessments. This work represents a significant advancement in the field, combining innovative methodologies with rigorous experimental validation to address longstanding challenges in speech enhancement.
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