Abstract—Keyword Spotting (KWS) in continuous speech is an emerging but challenging task that needs to deal with speech dynamics. Literature contains a variety of approaches for keyword spotting and spoken term detection. Most of these techniques are based on pattern based methods that are limited to a specific vocabulary of words with high computational cost for system training. A valuable contribution to the existing speaker dependent keyword spotting approaches is made in this paper by introducing a template matching based approach that exploits the Dempster’s theory of Combined Evidence (CEv). The CEv plays a significant role as a Decision Support System (DSS) by integrating the keyword match/mismatch beliefs from multiple resources and providing a combined score (belief). Finally, performance of the proposed approach is compared with the existing keyword spotting techniques by using statistical analysis of the experimental results.

Index Terms—Spoken term detection, template matching, decision support system, similarity measure.

The authors are with the School of Electrical Engineering & Computer Science, University of Bradford, BD7 1DP, United Kingdom.

Cite:Wasiq Khan, Rob Holton, "Decision Support System for Keyword Spotting Using Theory of Evidence," International Journal of Computer and Electrical Engineering vol. 8, no. 1, pp. 22-30, 2016.