Exploring the Psycholinguistic Impact of Stuttering through Artificial Intelligence

Abstract

Stuttering, a complex neurodevelopmental speech disorder, profoundly impacts an individual's communication and quality of life. We argue that stuttering is not merely a motor speech disorder but a multifaceted phenomenon deeply intertwined with cognitive, linguistic, and social processes. This study explores the complex interplay between stuttering, psycholinguistics, and artificial intelligence through the lens of Sociotechnical Systems and Sentiment Theory, leveraging Natural Language Processing to analyze and interpret human text. Employing a mixed-methods approach, this research combines quantitative and qualitative methods to provide a nuanced understanding of stuttering’s impact on communication and quality of life. The objectives will examine the psychological and linguistic impact. Through surveys, interviews, and sentiment scales, we investigate language attitudes, psycholinguistic processes, and adaptive communication strategies among individuals who stutter. Sentiment analysis as a methodology was conducted using Natural Language Processing (NLP) libraries to quantify the sentiment patterns in participants’ responses. Key findings reveal that stuttering significantly affects language processing, particularly in word retrieval and sentence formulation, and that individuals who stutter develop unique communication styles to mitigate its effects. AI, with its capabilities in speech analysis, natural language processing, and pattern recognition, offers unprecedented opportunities for objective assessment, personalized intervention, and deeper insights into the underlying mechanisms of stuttering. However, the successful integration of AI into stuttering research and therapy necessitates a comprehensive STS approach, ensuring joint optimization of both technological advancements and human elements, addressing ethical considerations, and fostering collaboration among diverse stakeholders.