DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization
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 importan
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 56%Introducing Real World VoiceEQ: Measuring the human quality of voice AI →
- FuzzySimilar title/name (fuzzy) · 87%huggingface/speech-to-speech →
“Fuzzy title match (0.94): “DAMOS: Learning Distortion-Aware Speech Quality Assessment t” ≈ “huggingface/speech-to-speech””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “DAMOS: Learning Distortion-Aware Speech Quality Assessment t” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Naiyuan Li →
“DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization”
- LinkedLinked via arxiv author · 85%Li Dong →
“DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization”
- LinkedLinked via arxiv author · 85%Diqun Yan →
“DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization”