Problem
Research questions and friction points this paper is trying to address.
speech quality assessment
distortion localization
Mean Opinion Scores
DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization
🤖 AI Summary
本文提出DAMOS框架，通过明确的失真定位来改进语音质量评估，解决了现有方法仅依赖于整体评分而缺乏局部失真信息的问题。
📝 Abstract
Automatic speech quality assessment aims to predict Mean Opinion Scores (MOS) consistent with human subjective perception and is essential for evaluating speech generation, enhancement, and communication systems. For speech signals, especially synthetic speech, distortions often occur locally, and overall perceptual quality is usually dominated by a small number of perceptually salient distortion regions. However, most existing methods are primarily optimized with utterance-level MOS, which provides only coarse-grained supervision and offer no explicit indication of where perceptually important distortions occur. To address this limitation, we introduce explicit distortion localization as auxiliary knowledge for speech quality assessment. We construct the first partially distorted speech dataset with frame-level distortion annotations and train a localization model to generate distortion cues. Building on these cues, we propose DAMOS, a distortion-aware speech quality assessment framework that integrates localization information into the MOS prediction pipeline. Experiments on multiple public benchmarks demonstrate that DAMOS consistently outperforms existing methods and exhibits strong cross-dataset generalization, validating the effectiveness of explicit distortion localization for speech quality assessment.
Innovation
Methods, ideas, or system contributions that make the work stand out.
explicit distortion localization
frame-level distortion annotation
distortion-aware speech quality assessment
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