Application of modified wavelet features and multi-class sphere SVM to pathological vocal detection

This paper researches the method of wavelet feature-vectors and multi-class support vector machines applied to pathological vocal detection, which extracts features of the pathological vocal based on continuous wavelet transformation and then classifies pathological vocal by multi - class support vector machine. In order to reduce computation complexity caused by using the standard support vector machines for multi-class classification, a new multi-class classification algorithm based on the idea of one-class classification is proposed. It can form a decision function for every single class sample and accordingly obtain the aim of classification based on maximum of decision function. Experimental results have shown that the pathological vocal detection system is feasible and applicable by the combination of multi-class SVM and wavelet feature-vectors.