Sparse KPCA for Feature Extraction in Speech Recognition

Abstract

This paper presents an analysis of the applicability of sparse kernel principal component analysis (SKPCA) for feature extraction in speech recognition, as well as a proposed approach to make the SKPCA technique realizable for a large amount of training data, which is a usual context in speech recognition systems. Although the KPCA (kernel principal component analysis) has proved to be an efficient technique for being applied to speech recognition, it has the disadvantage of requiring training data reduction, when its amount is excessively large. The standard approach to perform this data reduction is to randomly choose frames from the original data set, which does not necessarily provide a good statistical representation of the original data set. In order to solve this problem a likelihood related re-estimation procedure was applied to the KPCA framework, thus creating the SKPCA. The experimental results show the efficiency of SKPCA technique with the proposed approach over the KPCA with the standard sparse solution using randomly chosen frames and the standard feature extraction techniques.