Overlapping One-Class SVMs for Utterance Verification in Speech Recognition

Utterance verification is increasingly essential for robustness and better performance of speech recognition systems. In this paper, we use overlapping one-class SVMs to verify utterances and propose a K-means based training algorithm for the overlapping one-class SVMs. The training algorithm first divides the training data into several clusters based on the K-means algorithm and then expands each cluster by inserting some nearest outside data. Then it iteratively trains the overlapping one-class SVMs on the expanded clusters and constructs the train clusters based on the learned overlapping one-class SVMs until the train clusters remain unchanged. Experimental results on a real dataset show the overlapping one-class SVMs can greatly improve the recall of the speech recognition systems.