A Comprehensive Approach for Classification of Stuttering in Children

Abstract

Stuttering, a developmental speech disorder, poses significant challenges for children, impacting their fluency and interrupting the natural flow of speech. These disruptions have far-reaching consequences, particularly in communication, social interaction, and emotional well-being. Early intervention plays a crucial role in supporting optimal child development. This paper explores the potential of Machine Learning and Deep Learning techniques in the context of stuttering identification. By analyzing speech patterns and identifying specific characteristics associated with stuttering, these advanced computational methods offer valuable insights. Leveraging the power of these techniques, researchers and clinicians can enhance their understanding of stuttering, facilitate early detection, and develop effective interventions to support children affected by this speech disorder. This research paper aims to highlight the significance of applying Machine Learning and Deep Learning techniques as a promising approach for early intervention and improved outcomes in the field of stuttering identification and treatment in children. Keywords: Stuttering, Developmental Speech Disorder, Children, Telugu Language, Annotation, Stutter codes, Machine Learning, Deep Learning, SMOTE