[1] Al-Ansary. S (2014). “Towards a Large Scale Deep Semantically Analyzed Corpus for Arabic: Annotation and Evaluation”, Empirical Methods in Natural Language Processing
[2] Al-Ansary. S, Nagy. M, Adly.N (2013). A suite of tools for Arabic natural language processing: A UNL approach. 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), pp. (1-6.) 978-1-4673-2821-0/13.IEEE. Egypt.
[3] Al-Banna, A. K., Edirisinghe, E., Fang, H., & Hadi, W. (2022). Stuttering disfluency detection using machine learning approaches. Journal of Information & Knowledge Management, 21(02), pp. 2250020:1-2250020:16.
[4] Al-Budoor, N., & Peña, E. D. (2022). Identifying language disorder in bilingual children using automatic speech recognition. Journal of Speech, Language, and Hearing Research, 65(7), 2648-2661.
[5] American Speech-Language-Hearing Association. (2024). Fluency disorders. https://www.asha.org/practiceportal/clinical-topics/fluency-disorders/
[6] American Speech-Language-Hearing Association. (2016). Scope of practice in speech-language pathology. www.asha.org/policy/.
[7] Alnashwan R, Alhakbani N, Al-Nafjan A, Almudhi A, Al-Nuwaiser W. (2023). Computational intelligencebased stuttering detection: a systematic review. Diagnostics;13(23):3537. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10706171/
[8] Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International journal of social research methodology, 8(1), 19-32.
[9] Asci, F., Marsili, L., Suppa, A., Saggio, G., Michetti, E., Leo, P. D., Patera, M., Longo, L., Ruoppolo, G., & Gado, F. D. ( 2022). Acoustic Analysis in Stuttering: A Machine-Learning Study. Frontiers in Neurology, 14, 1169707.
[10] Azevedo. N, Kehayia. E, Jarema .G, Le Dorze.G, Beaujard.C & Marc Yvon (2023): How artificial intelligence (AI) is used in aphasia rehabilitation: A scoping review, Aphasiology, DOI: 10.1080/02687038.2023.2189513.
[11] Bamdad M, Zarshenas H, Auais MA.( 2015). Application of BCI systems in neurorehabilitation: a scoping review. Disability Rehabilitative Assistant Technology ;10:355-64. [Crossref] [PubMed]
[12] Beccaluva, E. A., Catania, F., Arosio, F., & Garzotto, F. (2023). Predicting developmental language disorders using artificial intelligence and a speech data analysis tool. Human–Computer Interaction, 1-35.
[13] Bhat, C., & Strik, H. (2020). Automatic assessment of sentence-level dysarthria intelligibility using BLSTM. IEEE Journal of Selected Topics in Signal Processing, 14(2), 322-33
[14] Birbaumer N, Weber C, Neuper C, et al. Physiological regulation of thinking brain-computer interface (BCI). (2006). Prog Brain Resources; 159:369-91. [Crossref] [PubMed].
[15] Castle, A. (2023). Can AI Provide Communication Assistance to People with Aphasia? Journal of Aphasia and speech /language pathology. Arizona State University. USA
[16] Compton, E. C., Cruz, T., Andreassen, M., Beveridge, S., Bosch, D., Randall, D. R., & Livingstone, D. (2023). Developing an artificial intelligence tool to predict vocal cord pathology in primary care settings. The Laryngoscope, 133(8), 1952-1960.
[17] Craig, A.; Blumgart, E.; Tran, Y. (2009). The Impact of Stuttering on the Quality of Life in Adults Who Stutter. Journal of Fluency Disorders., 34, 61–71. [CrossRef] 6.
[18] Day, M., Dey, R. K., Baucum, M., Paek, E. J., Park, H., & Khojandi, A. (2021), November). Predicting severity in people with aphasia: A natural language processing and machine learning approach. Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 2299-2302). IEEE
[19] D’Onofrio KL, Zeng FG. Tele-audiology: current state and future directions. (2024) Front Digit Health 3:788103. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784511/ [20] Fabry DA, Bhowmik A.K.(2021). Improving speech understanding and monitoring health with hearing aids using artificial intelligence and embedded sensors. PubMed 2021 Aug;42(3):295-308. doi: 10.1055/s-0041- 1735136. Epub 2021 Sep 24.42(3):295–308.
[21] Georgiou, G. P., & Theodorou, E. (2023). Detection of developmental language disorder in Cypriot Greek children using a machine learning neural network algorithm. preprint arXiv:2311.15054.
[22] Joshy, A. A., & Rajan, R. (2022). Automated dysarthria severity classification: A study on acoustic features and deep learning techniques. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 1147- 1157. ISSN 2349-7831 International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH) Vol. 11, Issue 1, pp: (109-119), Month: January - March 2024, Available at: www.paperpublications.org Page | 119 Paper Publications.
[23] Liss.J and Berisha.V (2020). How Will Artificial Intelligence Reshape Speech-Language Pathology Services and Practice in the Future? Arizona State University.
[24] Justice, L. M., Ahn, W.-Y., & Logan, J. A. (2019). Identifying children with clinical language disorder: an application of machine-learning classification. Journal of learning disabilities, 52(5), 351-365.
[25] Kourkounakis T., Hajavi A., Etemad A ( 2020) Detecting Multiple Speech Disfluencies Using a Deep Residual Network with Bidirectional Long Short-Term Memory. IEEE; Barcelona, Spain: p. 6093. [Google Scholar]
[26] Kendra Wormald (2023). The Role of Artificial Intelligence in Speech Therapy. Faculty of medicine. Toronto university
[27] Korinek, A.; Schindler, M.; Stiglitz (2021). Artificial Intelligence, and Inclusive Growth. Journal of Technological Progress, Washington, DC, USA,
[28] Kumar. T, Hostel.R, (2023). The Role of Artificial Intelligence in Diagnosis and Management of Laryngeal Disorders. Medical College, Kakinada, India.
[29] Lee MB, Kramer DR, Peng T, et al. (2019). Brain-Computer Interfaces in Quadriplegic Patients. Neurosurgical Clin N Am ;30:275-81. [Crossref] [PubMed]
[30] Mahmoud, S. S., Kumar, A., Li, Y., Tang, Y., & Fang, Q. (2021). Performance evaluation of machine learning frameworks for aphasia assessment. Sensors, 21(8), 2582.
[31] Melissa James, M. H. Sc.(2023). The Role of Artificial Intelligence in Speech Therapy. University of Toronto. Canada
[32] Milani, M. M., Ramashini, M., & Krishani, M. (2020). A real-time application to detect human voice disorders. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 979-984). IEEE.
[33] Murero . M, Vita. S, Mennitto. A, D’Ancona.G. (2020) Artificial Intelligence for Severe Speech Impairment: Innovative approaches to AAC and Communication University Federico II, (Italy).
[34] Parsa, M., Alam, M. R., & Mihailidis, A. (2021). Towards AI-powered language assessment tools. https://doi. org/10.21203/rs.3.rs-246079/v1
[35] Pierce, J. E. (2024). AI-Generated Images for Speech Pathology—An Exploratory Application to Aphasia Assessment and Intervention Materials. American Journal of Speech-Language Pathology, 33(1), 443-451.
[36] Pravin S.C.,& Palanivelan M. (2021). Regularized Deep LSTM Autoencoder for Phonological Deviation Assessment. Int. J. Patt. Recogn. Artif. Intell.;35:2152002. doi: 10.1142/S0218001421520029. [CrossRef] [Google Scholar]
[37] Pravin, S. C., & Palanivelan, M. (2022). WDSAE-DNDT Based Speech Fluency Disorder Classification. Malaysian Journal of Computer Science, 35(3), 222-242.
[38] Shah U, Alzubaidi M, Mohsen F, Abd-Alrazaq A, Alam T, Househ M. (2022). The role of artificial intelligence in decoding speech from eeg signals: a scoping review. Sensors (Basel) [Internet]. Sep 15 [cited 2024 Feb 26];22(18):6975. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505262/
[39] Sheikh, S.A.; Sahidullah, M.; Hirsch, F.; Ouni, S. (2023) .Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning. IEEE J. Biomed. Health Inform., 27, 2553–2564.
[40] Sheikh, S.A.; Sahidullah, M.; Hirsch, F.; Ouni, S. (2022). Machine Learning for Stuttering Identification: Review, Challenges and Future Directions. Neurocomputing, 514, 385–402.
[41] Song, J., Lee, J. H., Choi, J., Suh, M. K., Chung, M. J., Kim, Y. H., ... & Cho, J. W. (2022). Detection and differentiation of ataxic and hypokinetic dysarthria in cerebellar ataxia and parkinsonian disorders via wave splitting and integrating neural networks. PloS one, 17(6), e0268337. [42] Tartarisco, G., Bruschetta, R., Summa, S., Ruta, L., Favetta, M., Busa, M., ... & Pioggia, G. (2021). Artificial intelligence for dysarthria assessment in children with ataxia: A hierarchical approach. IEEE Access, 9, 166720- 166735. [43] Taylor.K &Sheik.W (2024) Automated hearing impairment diagnosis using machine-learning: An open-source software development undergraduate research project. Computer Applications in Engineering Education: Volume 32, Issue 3, May 2024 [44] Toki, E. I., Tatsis, G., Tatsis, V. A., Plachouras, K., Pange, J., & Tsoulos, I. G. (2023). Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication. Mathematics, 11(7), 1643. [45] Tricco, A. C., Lillie, E., Zarin, W., O’brien, K., Colquhoun, H., Kastner, M., Levac, D., Ng, C., Sharpe, J. P., & Wilson, K. (2016). A scoping review on the conduct and reporting of scoping reviews. BMC medical research methodology, 16, 1-10. [46] Verde, L., De Pietro, G., Alrashoud, M., Ghoneim, A., Al-Mutib, K. N., & Sannino, G. (2019). Leveraging artificial intelligence to improve voice disorder identification through the use of a reliable mobile app. IEEE Access, 7, 124048- 124054. [47] Weekes B.S., Chen M.J., Quns H.C., Lin Y.B., Yao C., Xiaos X.Y. Anomia and dyslexia in Chinese: A familiar story? Aphasiology. 1998;12:77–98. doi: 10.1080/02687039808249445. [48] Zhang, Z., Shang, X., Yang, L.-Z., Ai, W., Wang, J., Wang, H., Wong, S. T., Wang, X., & Li, H. (2023). Artificial Intelligence‐Powered Acoustic Analysis System for Dysarthria Severity Assessment. Advanced Intelligent Systems, 5(10), 2300097