Support vector machine-based stuttering dysfluency classification using GMM supervectors Online publication date: Sat, 18-Jul-2015
by P. Mahesha; D.S. Vinod
International Journal of Grid and Utility Computing (IJGUC), Vol. 6, No. 3/4, 2015
Abstract: 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 non-parametric 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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Grid and Utility Computing (IJGUC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook