Asymmetrical Support Vector Machines and applications in speech processing

Support Vector Machines have merged as a pattern classifier and have been shown to be successful in some tasks in the realm of speech processing. This paper explores the issues involved in applying SVMs to asymmetrical situations, namely. beavy sample ratio bias between different classes and different costs for different types of misclassification error. We also present our revisions on the SMO algorithm to make the asymmetrical SVM training procedure practical. Experiments on both recognition of isolated spoken digits in mandarin and the learning of the decision function for speaker authentication yielded performance improvements, which show the effectiveness of asymmetrical SVMs.