Extension of a Kernel-Based Classifier for Discriminative Spoken Keyword Spotting

Abstract

A keyword spotter is considered as a binary classifier that separates a class of utterances containing a target keyword from utterances without the keyword. These two classes are not inherently linearly separable. Thus, linear classifiers are not completely suitable for such cases. In this paper, we extend a kernel-based classification approach to separate the mentioned two non-linearly separable classes so that the area under the Receiver/Relative Operating Characteristic (ROC) curve (the most common measure for keyword spotter evaluation) is maximized. We evaluated the proposed keyword spotter under different experimental conditions on TIMIT database. The results indicate that, in false alarm per keyword per hour smaller than two, the true detection rate of the proposed kernel-based classification approach is about 15 % greater than that of the linear classifiers exploited in previous researches. Additionally, area under the ROC curve (AUC) of the proposed method is 1 % higher than AUC of the linear classifiers that is significant due to confidence levels 80 and 95 % obtained by t-test and F-test evaluations, respectively. In addition, we evaluated the proposed method in different noisy conditions. The results indicate that the proposed method show a good robustness in noisy conditions.