Candidate
Nesma Faris
Title
Speech Endpoint Detection: An Image Segmentation Approach
Supervisor(s)
Basir, Otman and Michailovich, Oleg
Abstract
Speech Endpoint Detection, also known as Speech Segmentation, is an unsolved problem in speech processing that affects numerous applications including robust speech recognition. This task is not as trivial as it appears, and most of the existing algorithms degrade at low signal-to-noise ratios (SNRs). During the last few decades, numerous researchers have developed different strategies for detecting speech in a noisy signal, and have evaluated the influence of the endpoint detection effectiveness on the performance of speech processing systems. Most of the approaches have focused on the development of robust algorithms with special attention being paid to the derivation and study of noise robust features and decision rules. This research tackles the endpoint detection problem in a different way, and proposes a novel speech endpoint detection algorithm which has been derived from Chan-Vese algorithm for image segmentation. The proposed algorithm has the ability to fuse multi features extracted from the speech signal to enhance the detection accuracy. The algorithm performance has been evaluated and compared to two widely used speech detection algorithms under various noise environments with SNR levels ranging from 0 dB to 30 dB. Furthermore, the proposed algorithm has also been applied to different types of American English phonemes. Experimental results are provided to further investigate and confirm the effectiveness and usefulness of the proposed algorithm.