  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.
