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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.02270 |
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| _version_ | 1866909885955833856 |
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| author | Jia, Kaimeng Tu, Minzhu Jin, Zengrui Wang, Siyin Zhang, Chao |
| author_facet | Jia, Kaimeng Tu, Minzhu Jin, Zengrui Wang, Siyin Zhang, Chao |
| contents | Dysarthria is a speech disorder characterized by impaired intelligibility and reduced communicative effectiveness. Automatic dysarthria assessment provides a scalable, cost-effective approach for supporting the diagnosis and treatment of neurological conditions such as Parkinson's disease, Alzheimer's disease, and stroke. This study investigates leveraging human perceptual annotations from speech synthesis assessment as reliable out-of-domain knowledge for dysarthric speech assessment. Experimental results suggest that such supervision can yield consistent and substantial performance improvements in self-supervised learning pre-trained models. These findings suggest that perceptual ratings aligned with human judgments from speech synthesis evaluations represent valuable resources for dysarthric speech modeling, enabling effective cross-domain knowledge transfer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02270 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Augmenting Open-Vocabulary Dysarthric Speech Assessment with Human Perceptual Supervision Jia, Kaimeng Tu, Minzhu Jin, Zengrui Wang, Siyin Zhang, Chao Audio and Speech Processing Dysarthria is a speech disorder characterized by impaired intelligibility and reduced communicative effectiveness. Automatic dysarthria assessment provides a scalable, cost-effective approach for supporting the diagnosis and treatment of neurological conditions such as Parkinson's disease, Alzheimer's disease, and stroke. This study investigates leveraging human perceptual annotations from speech synthesis assessment as reliable out-of-domain knowledge for dysarthric speech assessment. Experimental results suggest that such supervision can yield consistent and substantial performance improvements in self-supervised learning pre-trained models. These findings suggest that perceptual ratings aligned with human judgments from speech synthesis evaluations represent valuable resources for dysarthric speech modeling, enabling effective cross-domain knowledge transfer. |
| title | Augmenting Open-Vocabulary Dysarthric Speech Assessment with Human Perceptual Supervision |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2511.02270 |