Detecting Stuttering Types using Deep Learning

Abstract

This website was developed as an educational tool to train students in accurately identifying different types of stuttering. The platform provides audio samples, allowing users to practice distinguishing between various stutter types, such as repetitions, prolongations, and blocks. As students classify these speech patterns, their responses are recorded and stored, with hopes to eventually form a structured dataset. This dataset serves a dual purpose: enhancing student learning through hands-on experience and creating a valuable resource for future AI applications in speech therapy and automated stutter detection. The project aims to bridges the gap between AI and stutter disfluency detection. The resulting dataset can support the development of AI-driven tools for diagnosing and assisting individuals with speech disorders, ultimately improving accessibility to speech therapy solutions.