Applications of Artificial Intelligence for Cross-Language Intelligibility Assessment of Dysarthric Speech.
Purpose: Speech intelligibility is a critical outcome in the assessment and management of dysarthria, yet most research and clinical practices have focused on American English, limiting their applicability across languages. This commentary introduces a conceptual framework leveraging artificial inte...
| Publicado en: | Perspectives of the ASHA Special Interest Groups Vol. 10; no. 6; pp. 2298 - 2309 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
American Speech-Language-Hearing Association
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190171851&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190171851 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2381473X KTSD jtl: Perspectives of the ASHA Special Interest Groups issn: 2381473X maglogo: N pubinfo: dt: Dec2025 vid: 10 iid: 6 pid: 42 pub: American Speech-Language-Hearing Association place: Rockville, Maryland artinfo: ui: 190171851 190171851 190171851 10.1044/2025_PERSP-25-00030 190171851 ppf: 2298 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Applications of Artificial Intelligence for Cross-Language Intelligibility Assessment of Dysarthric Speech. aug: au: Yeo, Eunjung Liss, Julie M. Berisha, Visar Mortensen, David R. affil: Carnegie Mellon University, Pittsburgh, PA sug: subj: Dysarthria Diagnosis Speech Intelligibility Artificial Intelligence Methods Multilingualism Methods Speech Acoustics Physiology Human Conceptual Framework Phonetics Methods Voice Recognition Systems Automation Machine Learning Data Curation Language Evaluation Research Spain ab: Purpose: Speech intelligibility is a critical outcome in the assessment and management of dysarthria, yet most research and clinical practices have focused on American English, limiting their applicability across languages. This commentary introduces a conceptual framework 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. Results: We present a specific instantiation of the proposed framework with Spanish dysarthric speech, demonstrating its practical implementation. This example highlights the framework's potential to deliver reliable measures of speech intelligibility across languages. Conclusions: Cross-language intelligibility assessment of dysarthric speech requires approaches that address both language-universal dysarthric manifestations and language-specific factors to ensure accurate and clinically meaning evaluation across languages. Recent advances in AI provide the foundational tools to support this integration, shaping future directions toward cross-langauge intelligibility assessment frameworks that are efficient, scalable, and applicable across diverse languages. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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