Therapytalk: An AI-Driven Web-Based Speech Therapy Management System for Children

Authors

  • Vinay Kakad Undergraduate Student, Department of Artificial Intelligence & Data Science, P.R. Pote (Patil) College of Engineering & Management, Amravati, Maharashtra, India
  • Priyanka Bhusari Undergraduate Student, Department of Artificial Intelligence & Data Science, P.R. Pote (Patil) College of Engineering & Management, Amravati, Maharashtra, India
  • Tanvi Karale Undergraduate Student, Department of Artificial Intelligence & Data Science, P.R. Pote (Patil) College of Engineering & Management, Amravati, Maharashtra, India
  • Vanshika Dhonde Undergraduate Student, Department of Artificial Intelligence & Data Science, P.R. Pote (Patil) College of Engineering & Management, Amravati, Maharashtra, India
  • A.R. Ladole Assistant Professor, Department of Artificial Intelligence & Data Science, P.R. Pote (Patil) College of Engineering & Management, Amravati, Maharashtra, India
  • Pratik Angaitkar Assistant Professor, Department of Artificial Intelligence & Data Science, P.R. Pote (Patil) College of Engineering & Management, Amravati, Maharashtra, India
  • Ajay B. Gadicha Assistant Professor, Department of Artificial Intelligence & Data Science, P.R. Pote (Patil) College of Engineering & Management, Amravati, Maharashtra, India

Keywords:

Clinical management system, Digital healthcare application, Real-time progress monitoring, Speech language therapy, Web-based platform

Abstract

Effective communication is the foundation of a child's cognitive and social development, yet millions of children face speech challenges that hinder their growth. Traditional speech therapy, while essential, is often constrained by manual processes, a limited number of therapists, and assessment tools, making it less efficient. To bridge this gap, this paper introduces TherapyTalk, an AI-powered web-based clinical management system designed to revolutionize speech-language therapy for children aged 5 to 12. By leveraging artificial intelligence and machine learning, TherapyTalk automates the creation of personalized therapy plans, enables real-time progress monitoring, and provides data-driven assessments for therapy exercises. Additionally, the platform generates actionable clinical insights, empowering therapists with evidence-based decision support. By integrating digital innovation, TherapyTalk enhances therapy effectiveness, optimizes resource utilization, and offers a structured, scalable, and user-friendly approach transforming speech therapy into a more engaging and impactful experience for children and therapists.

References

Ong, Ashwini Namasivayam‐MacDonald, S. Kim, and Sophia Werden Abrams, “The use of music and music‐related elements in speech‐language therapy interventions for adults with neurogenic communication impairments: A scoping review,” International Journal of Language & Communication Disorders, Aug. 2024, doi: https://doi.org/10.1111/1460-6984.13104.

C. Alighieri, C. D. Coster, K. Bettens, and V. Pereira, “Does Generalization Occur Following Speech Therapy? A Study in Children with a Cleft Palate,” Journal of Speech Language and Hearing Research, vol. 68, no. 1, pp. 1–14, Dec. 2024, doi: https://doi.org/10.1044/2024_jslhr-24-00292.

A. A. Maier, C. Hacker, E. Nöth, E. Nkenke, F. Rosanowski, and M. W. Schuster, “Intelligibility of Children with Cleft Lip and Palate: Evaluation by Speech Recognition Techniques,” 18th International Conference on Pattern Recognition (ICPR’06), Jan. 2006, doi: https://doi.org/10.1109/icpr.2006.718.

C. Alighieri, K. Bettens, C. Scheerens, “Diagnosis and Treatment of Speech Disorders in Children with a Cleft (Lip and) Palate: A State-Of-The-Art Overview,” Journal of Craniofacial Surgery, Mar. 2025, doi: https://doi.org/10.1097/scs.0000000000011313.

F. Allemeersch, K. Van Lierde, N. Verhaeghe, K. Bettens, “Effectiveness and cost‐effectiveness of high‐intensity versus low‐intensity speech intervention in children with a cleft palate: Protocol for a randomized controlled trial,” International Journal of Language & Communication Disorders, vol. 60, no. 2, Feb. 2025, doi: https://doi.org/10.1111/1460-6984.70019

M. Нryntsiv, M. Zamishchak, Y. Bondarenko, H. Suprun, and A. Dushka, “Approaches to Speech Therapy for Children with Autism Spectrum Disorders (ASD),” International Journal of Child Health and Nutrition, vol. 14, no. 1, pp. 32–45, Feb. 2025, doi: https://doi.org/10.6000/1929-4247.2025.14.01.05

N. Jamal, S. Shanta, F. Mahmud, and M. Sha’abani, “Automatic speech recognition (ASR) based approach for speech therapy of aphasic patients: A review,” AIP Conference Proceedings, vol. 1883, no. 1, 2017, doi: https://doi.org/10.1063/1.5002046.

L. J. Beijer, “E-Learning-Based Speech Therapy: A Web Application for Speech Training,” Telemedicine and e-Health, vol. 16, no. 2, pp. 177–180, Mar. 2010, doi: https://doi.org/10.1089/tmj.2009.0104

K. Kreidler, J. Vuolo, and L. Goffman, “Children with Developmental Language Disorder Show Deficits in the Production of Musical Rhythmic Groupings,” Journal of Speech Language and Hearing Research, vol. 66, no. 11, pp. 4481–4496, Sep. 2023, doi: https://doi.org/10.1044/2023_jslhr-23-00197.

J. J. L. Maas, N. M. de Vries, J. IntHout, B. R. Bloem, and J. G. Kalf, “Effectiveness of remotely delivered speech therapy in persons with Parkinson’s disease – a randomised controlled trial,” Eclinicalmedicine, vol. 76, p. 102823, Oct. 2024, doi: https://doi.org/10.1016/j.eclinm.2024.102823.

C. Deka, A. Shrivastava, A. K. Abraham, and P. Chauhan, “AI-based automated speech therapy tools for persons with speech sound disorder: a systematic literature review,” Speech, language and hearing, vol. 28, no. 1, pp. 1–22, Jun. 2024, doi: https://doi.org/10.1080/2050571x.2024.2359274.

G. R. Ares, M. M. Alonso, R. Rigual, “Digital tool as speech and language therapy for patients with post-stroke aphasia,” Digital Health, vol. 11, Jan. 2025, doi: https://doi.org/10.1177/20552076251314551

A. K. Dubey, S. R. Mahadeva Prasanna, and S. Dandapat, “Spectral Analysis Based Objective Assessment of Hypernasality in Cleft Palate Speech: A Review,” Circuits Systems and Signal Processing, vol. 44, pp. 4133–4166, Feb. 2025, doi: https://doi.org/10.1007/s00034-025-03007-x.

C. A. Tommy and J.-L. Minoi, “Speech therapy mobile application for speech and language impairment children,” IEEE Xplore, Dec. 2016. Doi: https://doi.org/10.1109/IECBES.2016.7843442

S. S. Awad and C. Piechocki, “Speech therapy software on an open web platform,” 2014 10th International Computer Engineering Conference (ICENCO), pp. 53–56, Dec. 2014, doi: https://doi.org/10.1109/icenco.2014.7050431.

B. Aldosari, R. Babsai, A. Alanazi, and H. Aldosari, “The Progress of Speech Recognition in Health Care: Surgery as an Example,” Studies in Health Technology and Informatics, Jun. 2023, doi: https://doi.org/10.3233/shti230519.

A. B. Gadicha, V. B. Gadicha, and M. Zuhair, “Predictive Patient-Centric Healthcare,” Advances in Electronic Government, Digital Divide, and Regional Development, pp. 139–152, Jan. 2024, doi: https://doi.org/10.4018/978-1-6684-9596-4.ch007.

V. B. Bhagat, V. M. Thakare, and A. B. Gadicha, “Stochastic Approach to Govern the Efficient Framework for Big Data Analytics Using Machine Learning and Edge Computing,” Apple Academic Press eBooks, pp. 249–261, Jan. 2024, doi: https://doi.org/10.1201/9781003401841-17.

I. Klatte et al., “Collaborative working with Parents of Children with DLD in Speech and Language Therapy: Identifying Dutch Speech and Language Therapists’ barriers to enhancing practice,” Research in Developmental Disabilities, vol. 156, p. 104882, Nov. 2024, doi: https://doi.org/10.1016/j.ridd.2024.104882

A. B. Gadicha, V. Gadicha, and S. Bohra, “Implementation Tools for Generating Statistical Consequence Using Data Visualization Techniques,” Wiley Publications, pp. 1–20, Oct. 2022, doi: https://doi.org/10.1002/9781119792826.ch1.

P. V. Kale, A. B. Gadicha, and G. D. Dalvi, “Detection and Classification of Brain Tumor Using Machine Learning,” 2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), pp. 1–6, Jul. 2024, doi: https://doi.org/10.1109/icstsn61422.2024.10670906.

Published

2025-05-28