Artificial Intelligence and Computational Approaches in the Assessment and Treatment of Speech–Language Disorders

A special issue of Behavioral Sciences (ISSN 2076-328X). This special issue belongs to the section "Cognition".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1036

Special Issue Editor


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Guest Editor
Faculty of Medicine & CHU Sainte-Justine Research Center/Brain and Child Development Axis, University of Montreal, Montreal, QC H3T 1J4, Canada
Interests: speech–language pathology; neuropsychology; computational neuroscience; AI and machine learning in health
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Special Issue Information

Dear Colleagues,

This Special Issue aims to bring together cutting-edge interdisciplinary research at the intersection of artificial intelligence (AI), computational modeling, and speech–language pathology, with a focus on applications across the lifespan and across diverse linguistic and cultural contexts. As digital health technologies evolve, there is a growing need to explore how AI can enhance the assessment, monitoring, and treatment of speech and language disorders—from early childhood to older adulthood, and developmental conditions to acquired impairments.

We invite contributions from a broad range of fields, including but not limited to speech–language pathology, computer science, linguistics, cognitive neuroscience, rehabilitation sciences, human–computer interaction (HCI), and biomedical and health engineering. We especially encourage submissions that address equity, accessibility, and the ethical and policy implications of deploying AI in real-world clinical and educational settings.

This Special Issue seeks to highlight both the promise and the challenges of integrating AI and computational tools into practice, with an emphasis on

  • Ethically grounded data collection and annotation;
  • Explainable and transparent AI models;
  • Inclusive and culturally responsive applications;
  • Real-world usability and accessibility.

Topics of interest include (but are not limited to) the following:

  • AI-based diagnostic tools for developmental, acquired, and aging-related speech–language disorders.
  • Automated speech recognition systems tailored for disordered or atypical speech.
  • Deep learning models for identifying linguistic, acoustic, prosodic, and behavioral markers.
  • Reinforcement learning and adaptive systems for personalized or precision therapy.
  • Multimodal approaches integrating speech, language, emotion, motor behavior, and cognition.
  • Computational modeling of typical and atypical speech, language, and reading development.
  • Ethical, legal, and policy considerations in AI applications for healthcare and education.
  • AI-driven systems to support augmentative and alternative communication (AAC).
  • Cross-linguistic, cross-cultural, and low-resource language applications of AI.
  • Clinical decision support systems and therapist-facing AI-powered analytics.
  • AI for universal design and inclusive communication tools.

Justification for the Special Issue

The rapid expansion of large-scale linguistic, clinical, and behavioral datasets, alongside advances in machine learning and natural language processing, offers transformative opportunities for the field of speech–language pathology. However, there remains a substantial gap between research innovation and clinical adoption. Many AI tools lack transparency, cultural adaptability, or real-world relevance, hindering their acceptance by clinicians, educators, and families.

This Special Issue will provide a platform to

  • Showcase innovative applications of AI that are clinically meaningful, culturally inclusive, and ethically responsible.
  • Critically examine the barriers to translating computational approaches into accessible and equitable care.
  • Stimulate dialogue between researchers, clinicians, technologists, and policy makers.

We welcome diverse submission types, including original research, methodological innovations, theoretical perspectives, case studies, and critical reviews, that contribute to advancing the responsible integration of AI into the care and support of individuals with communication disorders.

Dr. Selçuk Güven
Guest Editor

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Keywords

  • artificial intelligence
  • speech–language pathology
  • computational modeling
  • personalized therapy
  • explainable AI
  • multilingual and cross-cultural applications
  • clinical decision support

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Published Papers (1 paper)

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Research

17 pages, 306 KB  
Article
Multimodal AI Screening of Developmental Language Disorder in Tunisian Arabic Children: Clinical Markers and Computational Detection
by Faten Bouhajeb, Redha Touati and Selçuk Güven
Behav. Sci. 2026, 16(3), 375; https://doi.org/10.3390/bs16030375 - 6 Mar 2026
Viewed by 611
Abstract
Developmental Language Disorder (DLD) is a common neurodevelopmental condition that affects language acquisition in children. However, standardized diagnostic tools for Tunisian Arabic, a widely spoken yet underrepresented dialect, is still lacking. This study presents a multimodal biomedical informatics framework that integrates clinical assessments, [...] Read more.
Developmental Language Disorder (DLD) is a common neurodevelopmental condition that affects language acquisition in children. However, standardized diagnostic tools for Tunisian Arabic, a widely spoken yet underrepresented dialect, is still lacking. This study presents a multimodal biomedical informatics framework that integrates clinical assessments, speech recordings, and artificial intelligence (AI) for early DLD detection. Three linguistic tasks (the CLT Task, the Arabic Verb Evaluation Task, and the Nonword Repetition Task) were adapted for Tunisian Arabic, and spontaneous speech samples were collected from children with typical development and those with DLD. Statistical analyses revealed significant deficits in verb production, past-tense morphology, and phonological memory in the DLD group. For automated screening, we developed two systems: a Random Forest classifier based on structured clinical and linguistic features and a multimodal deep learning model using Wav2Vec2 acoustic embeddings. The best model achieved an F1 score of 0.85, demonstrating the feasibility of AI-assisted DLD screening. This work introduces the first standardized dataset and computational baseline for DLD in Tunisian Arabic, providing clinically relevant tools for early identification and supporting research on underrepresented Arabic dialects. This work also highlights future implications, including potential applications in early screening, the integration of acoustic markers, and the development of culturally adapted assessment tools for underrepresented languages. Full article
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