Stutter Diagnosis and Therapy System Based on Deep Learning

TL;DR

This paper presents a deep learning-based system for automatic stutter diagnosis and therapy recommendation, utilizing Gated Recurrent CNNs and SVMs to assess speech dysfluencies and suggest personalized treatments.

Contribution

It introduces a novel integrated system combining deep learning and machine learning techniques for comprehensive stutter assessment and therapy suggestion.

Findings

Effective detection of stutter severity and type

Accurate therapy recommendations based on speech features

Promising results demonstrating system viability

Abstract

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.