A novel spoken keyword spotting system using support vector machine

Abstract

Spoken keyword spotting is crucial to classify expertly a lot of hours of audio stuffing such as meetings and radio news. These systems are technologically advanced with the purpose of indexing huge audio databases or of differentiating keywords in uninterrupted speech streams. The proposed work involves sliding a frame-based keyword template along the speech signal and using support vector machine (SVM) misclassification rates obtained from the hyperplane of two classes efficiently search for a match. This work framed a novel spoken keyword detection algorithm. The experimental results show that the proposed approach competes with the keyword detection methods described in the literature and it is an alternative technique to the prevailing keyword detection approaches.