Segment-Level Pyramid Match Kernels For The Classification of Varying Length Patterns of Speech Using SVMs

Classification of long duration speech, represented as varying length sets of feature vectors using support vector machine (SVM) requires a suitable kernel. In this paper we propose a novel segment-level pyramid match kernel (SLPMK) for the classification of varying length patterns of long duration speech represented as sets of feature vectors. This kernel is designed by partitioning the speech signal into increasingly finer segments and matching the corresponding segments. We study the performance of the SVM-based classifiers using the proposed SLPMKs for speech emotion recognition and speaker identification and compare with that of the SVM-based classifiers using other dynamic kernels.