Computational Intelligence-Based Stuttering Detection: A Systematic Review

Raghad Alnashwan; Noura Alhakbani; Abeer Al-Nafjan; Abdulaziz Almudhi; Waleed Al-Nuwaiser

doi:10.3390/diagnostics13233537

Computational Intelligence-Based Stuttering Detection: A Systematic Review

Diagnostics (Basel). 2023 Nov 27;13(23):3537. doi: 10.3390/diagnostics13233537.

Authors

Raghad Alnashwan¹, Noura Alhakbani¹, Abeer Al-Nafjan², Abdulaziz Almudhi³, Waleed Al-Nuwaiser²

Affiliations

¹ Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
² Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
³ Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha 62529, Saudi Arabia.

Abstract

Stuttering is a widespread speech disorder affecting people globally, and it impacts effective communication and quality of life. Recent advancements in artificial intelligence (AI) and computational intelligence have introduced new possibilities for augmenting stuttering detection and treatment procedures. In this systematic review, the latest AI advancements and computational intelligence techniques in the context of stuttering are explored. By examining the existing literature, we investigated the application of AI in accurately determining and classifying stuttering manifestations. Furthermore, we explored how computational intelligence can contribute to developing innovative assessment tools and intervention strategies for persons who stutter (PWS). We reviewed and analyzed 14 refereed journal articles that were indexed on the Web of Science from 2019 onward. The potential of AI and computational intelligence in revolutionizing stuttering assessment and treatment, which can enable personalized and effective approaches, is also highlighted in this review. By elucidating these advancements, we aim to encourage further research and development in this crucial area, enhancing in due course the lives of PWS.

Keywords: machine learning; rehabilitation; stuttering detection; systematic review.

Publication types

Review

Grants and funding

This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RG23151).