Support vector machine-based stuttering dysfluency classification using GMM supervectors

It is generally acknowledged that recognition and classification of dysfluencies are an important criterion in the objective and accurate assessment of stuttered speech. For this reason, there is a growing interest in the application of Automatic Speech Recognition (ASR) technology to automate the dysfluency recognition. In this perspective, several studies have been carried out on the classification of dysfluencies by means of acoustic analysis, parametric and nonparametric feature extraction and statistical methods. This work is focused on introducing and evaluating Support Vector Machine (SVM) based dysfluency recognition system using a Gaussian Mixture Model (GMM) supervector. The experimental evaluation of the proposed system reveals that an SVM-based GMM supervector is effective for dysfluency classification. We have obtained substantial improvements in the performance by considering cepstral and their delta features.