An evolutionary based discriminative system for keyword spotting

Keyword spotting refers to detection of all occurrences of any given word in a speech utterance. In this paper, we define the keyword spotting problem as a binary classification problem and propose a discriminative approach for solving it. Our approach exploits evolutionary algorithm to determine the separating hyper plane between two classes: class of sentences containing the target keywords and class of sentences which don´t include the target keywords. The results on TIMIT indicate that the proposed method has good performance equal to 95.7 FOM value (average true detection rate for different false alarm per keyword per hour) and acceptable speed equal to 3.3 RTF (Real Time Factor) value.