Aligning Generative Speech Enhancement with Human Preferences via Direct Preference Optimization
This addresses the misalignment between objective metrics and human perception in speech enhancement, offering a novel training approach for improved perceptual quality.
This paper tackles the problem of speech enhancement by aligning generative models with human perceptual preferences using Direct Preference Optimization, achieving relative gains of up to 56% on speech quality metrics.
This work investigates speech enhancement (SE) from the perspective of language models (LMs). We propose a novel method that leverages Direct Preference Optimization (DPO) to improve the perceptual quality of enhanced speech. Using UTMOS, a neural MOS prediction model, as a proxy for human ratings, our approach guides optimization toward perceptually preferred outputs. This differs from existing LM-based SE methods that focus on maximizing the likelihood of clean speech tokens, which may misalign with human perception and degrade quality despite low prediction error. Experiments on the 2020 Deep Noise Suppression Challenge test sets demonstrate that applying DPO to a pretrained LM-based SE model yields consistent improvements across various speech quality metrics, with relative gains of up to 56%. To our knowledge, this is the first application of DPO to SE and the first to incorporate proxy perceptual feedback into LM-based SE training, pointing to a promising direction for perceptually aligned SE.