Feature extraction and normalization in SVM speaker verification using telephone speech

In this research the Support Vector Machine classifier is applied to a text independent speaker verification task using conversational telephone speech from the NIST 2000 Speaker Recognition Evaluation. The SVM is a discriminative classifier with good generalization characteristics. It has been shown to perform as well as, and sometimes outperform the more widely used Gaussian Mixture Model. The SVM, like other classifiers is vulnerable to environmental noise, distortions from transmission over communication channels such as the telephone channel, and intersession variability.

Grantor dc:publisher.institution

Department of Electrical Engineering

Year dc:date.issued

2007

Author dc:creator

• Mazibuko, Thembisile Thulisile

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/5167

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/5167