Outerproduct of trajectory matrix for acoustic modeling using support vector machines

Abstract

In this paper, we address the issues in classification of varying duration segments of speech using support vector machines. Commonly used methods for mapping the varying duration segments into fixed dimension patterns may lead to loss of crucial information necessary for classification. We propose a method in which the representation of a segment of speech is considered as a trajectory in a multidimensional space. A fixed dimension pattern vector derived from the outerproduct operation on the matrix representation of a multidimensional trajectory is given as input to the support vector machines. For acoustic modeling of speech segments consisting of multiple phonemes, the outerproduct operation is carried out for the trajectory matrix of each phoneme. The effectiveness of the proposed methods is demonstrated in recognition of isolated utterances of the E-set of English alphabet