Applications of Artificial Intelligence for Cross-language Intelligibility Assessment of Dysarthric Speech
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909604050370560 |
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| author | Yeo, Eunjung Liss, Julie Berisha, Visar Mortensen, David |
| author_facet | Yeo, Eunjung Liss, Julie Berisha, Visar Mortensen, David |
| contents | Purpose: Speech intelligibility is a critical outcome in the assessment and management of dysarthria, yet most research and clinical practices have focused on English, limiting their applicability across languages. This commentary introduces a conceptual framework--and a demonstration of how it can be implemented--leveraging artificial intelligence (AI) to advance cross-language intelligibility assessment of dysarthric speech. Method: We propose a two-tiered conceptual framework consisting of a universal speech model that encodes dysarthric speech into acoustic-phonetic representations, followed by a language-specific intelligibility assessment model that interprets these representations within the phonological or prosodic structures of the target language. We further identify barriers to cross-language intelligibility assessment of dysarthric speech, including data scarcity, annotation complexity, and limited linguistic insights into dysarthric speech, and outline potential AI-driven solutions to overcome these challenges. Conclusion: Advancing cross-language intelligibility assessment of dysarthric speech necessitates models that are both efficient and scalable, yet constrained by linguistic rules to ensure accurate and language-sensitive assessment. Recent advances in AI provide the foundational tools to support this integration, shaping future directions toward generalizable and linguistically informed assessment frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15858 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applications of Artificial Intelligence for Cross-language Intelligibility Assessment of Dysarthric Speech Yeo, Eunjung Liss, Julie Berisha, Visar Mortensen, David Computation and Language Sound Audio and Speech Processing Purpose: Speech intelligibility is a critical outcome in the assessment and management of dysarthria, yet most research and clinical practices have focused on English, limiting their applicability across languages. This commentary introduces a conceptual framework--and a demonstration of how it can be implemented--leveraging artificial intelligence (AI) to advance cross-language intelligibility assessment of dysarthric speech. Method: We propose a two-tiered conceptual framework consisting of a universal speech model that encodes dysarthric speech into acoustic-phonetic representations, followed by a language-specific intelligibility assessment model that interprets these representations within the phonological or prosodic structures of the target language. We further identify barriers to cross-language intelligibility assessment of dysarthric speech, including data scarcity, annotation complexity, and limited linguistic insights into dysarthric speech, and outline potential AI-driven solutions to overcome these challenges. Conclusion: Advancing cross-language intelligibility assessment of dysarthric speech necessitates models that are both efficient and scalable, yet constrained by linguistic rules to ensure accurate and language-sensitive assessment. Recent advances in AI provide the foundational tools to support this integration, shaping future directions toward generalizable and linguistically informed assessment frameworks. |
| title | Applications of Artificial Intelligence for Cross-language Intelligibility Assessment of Dysarthric Speech |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2501.15858 |