A Family of Example-Specific Density Based Matching Kernels for Classification of Varying Length Patterns of Speech Using Support Vector Machines

In this paper, we propose example-specific density based matching kernel (ESDMK) for the classification of varying length patterns of long duration speech represented as sets of feature vectors. The proposed kernel is computed between the pair of examples, represented as sets of feature vectors, by matching the estimates of the example-specific densities computed at every feature vector in those two examples. In this work, the number of feature vectors of an example among the K nearest neighbors of a feature vector is considered as an estimate of the example-specific density. The minimum of the estimates of two example-specific densities, one for each example, at a feature vector is considered as the matching score. The ESDMK is then computed as the sum of the matching score computed at every feature vector in a pair of examples. We also propose to compute pyramid match ESDMK by considering the increasingly larger number of neighbors to form pyramid of neighbors. We study the performance of the support vector machine (SVM) based classifiers using the proposed ESDMK and pyramid match ESDMK for speech emotion recognition and speaker identification tasks and compare the same with that of the SVM-based classifiers using the state-of-the-art kernels for varying length patterns.