Advances and Challenges in Artificial Intelligence and Machine Learning for Multilingual Speech Disorder Diagnosis and Therapy: An Integrative Review
DOI:
10.70211/ltsm.v3i2.229Published:
2025-08-31Abstract
This study examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in the diagnosis, assessment, and management of speech disorders, focusing on enhancing clinical practices. A comprehensive review of existing studies was conducted, highlighting the application of AI technologies such as Automated Speech Recognition (ASR), Natural Language Processing (NLP), and deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings reveal that AI and ML techniques have significantly improved diagnostic precision, therapeutic interventions, and clinical efficiency, especially in underserved populations. However, challenges related to limited multilingual datasets, model generalizability, and the interpretability of deep learning models were identified. Despite the promising advancements, challenges such as data privacy, model bias, and ethical concerns need to be addressed for broader clinical integration. This study contributes by synthesizing current AI and ML applications in speech disorder management, identifying key challenges, and proposing future directions, including the development of multilingual datasets, Explainable AI (XAI), and the integration of multimodal data to further enhance diagnostic and therapeutic outcomes.
Keywords:
Artificial Intelligence Machine Learning Speech Disorders Speech Therapy Natural Language Processing Deep Learning Diagnostic PrecisionReferences
H. B. S. Knudsen et al., "Allocation and funding of Speech and Language Therapy for children with Developmental Language Disorders across Europe and beyond," Res. Dev. Disabil., vol. 121, p. 104139, Feb. 2022. https://doi.org/10.1016/j.ridd.2021.104139
M. Aldè, U. Ambrosetti, S. Barozzi, and S. Aldè, "The Ongoing Challenges of Hearing Loss: Stigma, Socio-Cultural Differences, and Accessibility Barriers," Audiol. Res., vol. 15, no. 3, p. 46, Apr. 2025. https://doi.org/10.3390/audiolres15030046
G. P. Usha and J. S. R. Alex, "Speech assessment tool methods for speech impaired children: a systematic literature review on the state-of-the-art in Speech impairment analysis," Multimed. Tools Appl., vol. 82, no. 22, pp. 35021-35058, Sep. 2023. https://doi.org/10.1007/s11042-023-14913-0
D. Denman, R. Cordier, N. Munro, J.-H. Kim, and R. Speyer, "Standardized Measures Used Regularly by Speech-Language Pathologists when Assessing the Language Abilities of School-Aged Children: A Survey," Folia Phoniatr. Logop., vol. 75, no. 5, pp. 334-349, 2023. https://doi.org/10.1159/000530718
Lohmander, K. Klintö, E. Schalling, A. Szabo Portela, K. Johansson, and A. McAllister, "Students take charge of Learning - Using e-learning in Perceptual Assessment in Speech-Language Pathology," Scand. J. Educ. Res., vol. 65, no. 3, pp. 468-480, Apr. 2021. https://doi.org/10.1080/00313831.2020.1716064
Y. Yu, D. Appiah, B. Zulu, and K. A. Adu-Poku, "Integrating Rural Development, Education, and Management: Challenges and Strategies," Sustainability, vol. 16, no. 15, p. 6474, Jul. 2024. https://doi.org/10.3390/su16156474
X. Wang, G. W. Young, M. Z. Iqbal, and C. M. Guckin, "The potential of extended reality in Rural Education's future - perspectives from rural educators," Educ. Inf. Technol., vol. 29, no. 7, pp. 8987-9011, May 2024. https://doi.org/10.1007/s10639-023-12169-7
N. Kaiser and C. K. Barstow, "Rural Transportation Infrastructure in Low- and Middle-Income Countries: A Review of Impacts, Implications, and Interventions," Sustainability, vol. 14, no. 4, p. 2149, Feb. 2022. https://doi.org/10.3390/su14042149
Z. Qian, K. Xiao, and C. Yu, "A survey of technologies for automatic Dysarthric speech recognition," EURASIP J. Audio, Speech, Music Process., vol. 2023, no. 1, p. 48, Nov. 2023. https://doi.org/10.1186/s13636-023-00318-2
Z. Brahmi, M. Mahyoob, M. Al-Sarem, J. Algaraady, K. Bousselmi, and A. Alblwi, "Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review," Psychol. Res. Behav. Manag., vol. 17, pp. 2205-2232, May 2024. https://doi.org/10.2147/PRBM.S460283
D.-H. Shih, C.-H. Liao, T.-W. Wu, X.-Y. Xu, and M.-H. Shih, "Dysarthria Speech Detection Using Convolutional Neural Networks with Gated Recurrent Unit," Healthcare, vol. 10, no. 10, p. 1956, Oct. 2022. https://doi.org/10.3390/healthcare10101956
R. S. Calabrò and S. Mojdehdehbaher, "AI-Driven Telerehabilitation: Benefits and Challenges of a Transformative Healthcare Approach," AI, vol. 6, no. 3, p. 62, Mar. 2025. https://doi.org/10.3390/ai6030062
C.-C. Lin, A. Y. Q. Huang, and O. H. T. Lu, "Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review," Smart Learn. Environ., vol. 10, no. 1, p. 41, Aug. 2023. https://doi.org/10.1186/s40561-023-00260-y
J. R. Green, "Artificial Intelligence in Communication Sciences and Disorders: Introduction to the Forum," J. Speech, Lang. Hear. Res., vol. 67, no. 11, pp. 4157-4161, Nov. 2024. https://doi.org/10.1044/2024_JSLHR-24-00594
D. Deffner, J. M. Rohrer, and R. McElreath, "A Causal Framework for Cross-Cultural Generalizability," Adv. Methods Pract. Psychol. Sci., vol. 5, no. 3, Jul. 2022. https://doi.org/10.1177/25152459221106366
P. Radanliev, "AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development," Appl. Artif. Intell., vol. 39, no. 1, Dec. 2025. https://doi.org/10.1080/08839514.2025.2463722
L. Qin et al., "A survey of multilingual large language models," Patterns, vol. 6, no. 1, p. 101118, Jan. 2025. https://doi.org/10.1016/j.patter.2024.101118
V. Hassija et al., "Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence," Cognit. Comput., vol. 16, no. 1, pp. 45-74, Jan. 2024. https://doi.org/10.1007/s12559-023-10179-8
S. Ebneyamini and M. R. Sadeghi Moghadam, "Toward Developing a Framework for Conducting Case Study Research," Int. J. Qual. Methods, vol. 17, no. 1, Dec. 2018. https://doi.org/10.1177/1609406918817954
Y.-Y. Lin et al., "A Speech Command Control-Based Recognition System for Dysarthric Patients Based on Deep Learning Technology," Appl. Sci., vol. 11, no. 6, p. 2477, Mar. 2021. https://doi.org/10.3390/app11062477
S. Alharbi, M. Hasan, A. J. H. Simons, S. Brumfitt, and P. Green, "Sequence labeling to detect stuttering events in read speech," Comput. Speech Lang., vol. 62, p. 101052, 2020. https://doi.org/10.1016/j.csl.2019.101052
W.-S. Hsu, G.-T. Lin, and W.-H. Wang, "Enhancing Dysarthric Voice Conversion with Fuzzy Expectation Maximization in Diffusion Models for Phoneme Prediction," Diagnostics, vol. 14, no. 23, p. 2693, Nov. 2024. https://doi.org/10.3390/diagnostics14232693
C. K. Themistocleous, M. Andreou, and E. Peristeri, "Autism Detection in Children: Integrating Machine Learning and Natural Language Processing in Narrative Analysis," Behav. Sci. (Basel)., vol. 14, no. 6, p. 459, May 2024. https://doi.org/10.3390/bs14060459
W.-Z. Zheng, J.-Y. Han, C.-K. Lee, Y.-Y. Lin, S.-H. Chang, and Y.-H. Lai, "Phonetic posteriorgram-based voice conversion system to improve speech intelligibility of dysarthric patients," Comput. Methods Programs Biomed., vol. 215, p. 106602, Mar. 2022. https://doi.org/10.1016/j.cmpb.2021.106602
R. Fusaroli, E. Weed, R. Rocca, D. Fein, and L. Naigles, "Caregiver linguistic alignment to autistic and typically developing children: A natural language processing approach illuminates the interactive components of language development," Cognition, vol. 236, p. 105422, Jul. 2023. https://doi.org/10.1016/j.cognition.2023.105422
E. Conti et al., "Autism Spectrum Disorder and Childhood Apraxia of Speech: Early Language-Related Hallmarks across Structural MRI Study," J. Pers. Med., vol. 10, no. 4, p. 275, Dec. 2020. https://doi.org/10.3390/jpm10040275
C. Fraza, S. Rutherford, B. R. Bučková, C. F. Beckmann, and A. F. Marquand, "The promise of quantifying individual risk for brain disorders through normative modeling, a narrative review," Neurosci. Biobehav. Rev., vol. 176, p. 106284, Sep. 2025. https://doi.org/10.1016/j.neubiorev.2025.106284
E. Majda-Zdancewicz, A. Potulska-Chromik, M. Nojszewska, and A. Kostera-Pruszczyk, "Speech Signal Analysis in Patients with Parkinson's Disease, Taking into Account Phonation, Articulation, and Prosody of Speech," Appl. Sci., vol. 14, no. 23, p. 11085, Nov. 2024. https://doi.org/10.3390/app142311085
P. Dubey, P. Dubey, R. Raja, and S. S. Kshatri, "Bridging language gaps: The role of NLP and speech recognition in oral english instruction," MethodsX, vol. 14, p. 103359, Jun. 2025. https://doi.org/10.1016/j.mex.2025.103359
E. F. Hunt, C. Nang, S. J. Meldrum, and E. Armstrong, "Assessment of multilingual children by Western Australian speech-language pathologists: A survey of practices, barriers and facilitators," Int. J. Speech. Lang. Pathol., vol. 27, no. 3, pp. 370-384, May 2025. https://doi.org/10.1080/17549507.2025.2499516
F. Hasan Saif, M. N. Al-Andoli, and W. M. Y. W. Bejuri, "Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications," Appl. Sci., vol. 14, no. 22, p. 10121, Nov. 2024. https://doi.org/10.3390/app142210121
Z. Amiri, A. Heidari, N. J. Navimipour, M. Esmaeilpour, and Y. Yazdani, "The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review," Neural Comput. Appl., vol. 36, no. 11, pp. 5757-5797, Apr. 2024. https://doi.org/10.1007/s00521-023-09366-3
J. Lever et al., "Facing & mitigating common challenges when working with real-world data: The Data Learning Paradigm," J. Comput. Sci., vol. 85, p. 102523, Feb. 2025. https://doi.org/10.1016/j.jocs.2024.102523
R. A. Rasul, P. Saha, D. Bala, S. M. R. U. Karim, M. I. Abdullah, and B. Saha, "An evaluation of machine learning approaches for early diagnosis of autism spectrum disorder," Healthc. Anal., vol. 5, p. 100293, Jun. 2024. https://doi.org/10.1016/j.health.2023.100293
O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, "State-of-the-art in artificial neural network applications: A survey," Heliyon, vol. 4, no. 11, p. e00938, Nov. 2018. https://doi.org/10.1016/j.heliyon.2018.e00938
S. Gupta et al., "Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans," Diagnostics, vol. 14, no. 14, p. 1534, Jul. 2024. https://doi.org/10.3390/diagnostics14141534
D. Kim, Y. Lee, K. Chin, P. J. Mago, H. Cho, and J. Zhang, "Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting," Sustainability, vol. 15, no. 3, p. 2340, Jan. 2023. https://doi.org/10.3390/su15032340
H. Ding, H. Hou, L. Wang, X. Cui, W. Yu, and D. I. Wilson, "Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety," Foods, vol. 14, no. 2, p. 247, Jan. 2025. https://doi.org/10.3390/foods14020247
Y. Elsahar, S. Hu, K. Bouazza-Marouf, D. Kerr, and A. Mansor, "Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability," Sensors, vol. 19, no. 8, p. 1911, Apr. 2019. https://doi.org/10.3390/s19081911
M. Angrick et al., "Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS," Jul. 2023. https://doi.org/10.1101/2023.06.30.23291352
L. Nichol, S. J. Wallace, R. Pitt, A. D. Rodriguez, Z. Z. Diong, and A. J. Hill, "People with aphasia share their views on self-management and the role of technology to support self-management of aphasia," Disabil. Rehabil., vol. 44, no. 24, pp. 7399-7412, Nov. 2022. https://doi.org/10.1080/09638288.2021.1989501
V. Kumar, A. R. Ashraf, and W. Nadeem, "AI-powered marketing: What, where, and how?," Int. J. Inf. Manage., vol. 77, p. 102783, Aug. 2024. https://doi.org/10.1016/j.ijinfomgt.2024.102783
Z. Sadeghi et al., "A review of Explainable Artificial Intelligence in healthcare," Comput. Electr. Eng., vol. 118, p. 109370, Aug. 2024. https://doi.org/10.1016/j.compeleceng.2024.109370
D. Mienye et al., "A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges," Informatics Med. Unlocked, vol. 51, p. 101587, 2024. https://doi.org/10.1016/j.imu.2024.101587
Y. Xia, S.-Y. Shin, and J.-C. Kim, "Cross-Cultural Intelligent Language Learning System (CILS): Leveraging AI to Facilitate Language Learning Strategies in Cross-Cultural Communication," Appl. Sci., vol. 14, no. 13, p. 5651, Jun. 2024. https://doi.org/10.3390/app14135651
I. Roumeliotis, N. D. Tselikas, and D. K. Nasiopoulos, "Leveraging Large Language Models in Tourism: A Comparative Study of the Latest GPT Omni Models and BERT NLP for Customer Review Classification and Sentiment Analysis," Information, vol. 15, no. 12, p. 792, Dec. 2024. https://doi.org/10.3390/info15120792
L. Pascoe et al., "Strategic Considerations for Selecting Artificial Intelligence Solutions for Institutional Integration: A Single-Center Experience," Mayo Clin. Proc. Digit. Heal., vol. 2, no. 4, pp. 665-676, Dec. 2024. https://doi.org/10.1016/j.mcpdig.2024.10.004
W. Ziegler et al., "Speech and Nonspeech Parameters in the Clinical Assessment of Dysarthria: A Dimensional Analysis," Brain Sci., vol. 13, no. 1, p. 113, Jan. 2023. https://doi.org/10.3390/brainsci13010113
Jelassi, K. Matteli, H. Ben Khalfallah, and J. Demongeot, "Enhancing Personalized Mental Health Support Through Artificial Intelligence: Advances in Speech and Text Analysis Within Online Therapy Platforms," Information, vol. 15, no. 12, p. 813, Dec. 2024. https://doi.org/10.3390/info15120813
Author Biographies
Husnain Mukhdoom, Famous Forum of Worldwide Collaborators
President
.png)
S.R. Rajkumar, St. Joseph’s College
Head & Librarian
License
Copyright (c) 2025 Husnain Mukhdoom, S.R. Rajkumar
This work is licensed under a Creative Commons Attribution 4.0 International License.
