Stutter Diagnosis and Therapy System Based on Deep Learning
This addresses the need for automated stuttering assessment and therapy recommendations for individuals with communication disorders, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of diagnosing stuttering severity and type using a deep learning system, achieving results that help recommend personalized speech therapies.
Stuttering, also called stammering, is a communication disorder that breaks the continuity of the speech. This program of work is an attempt to develop automatic recognition procedures to assess stuttered dysfluencies and use these assessments to filter out speech therapies for an individual. Stuttering may be in the form of repetitions, prolongations or abnormal stoppages of sounds and syllables. Our system aims to help stutterers by diagnosing the severity and type of stutter and also by suggesting appropriate therapies for practice by learning the correlation between stutter descriptors and the effectiveness of speech therapies on them. This paper focuses on the implementation of a stutter diagnosis agent using Gated Recurrent CNN on MFCC audio features and therapy recommendation agent using SVM. It also presents the results obtained and various key findings of the system developed.