A study of supervised intrinsic spectral analysis for TIMIT phone classification

Abstract

Intrinsic Spectral Analysis (ISA) has been formulated within a manifold learning setting allowing natural extensions to out-of-sample data together with feature reduction in a learning framework. In this paper, we propose two approaches to improve the performance of supervised ISA, and then we examine the effect of applying Linear Discriminant technique in the intrinsic subspace compared with the extrinsic one. In the interest of reducing complexity, we propose a preprocessing operation to find a small subset of data points being well representative of the manifold structure; this is accomplished by maximizing the quadratic Renyi entropy. Furthermore, we use class based graphs which not only simplify our problem but also can be helpful in a classification task. Experimental results for phone classification task on TIMIT dataset showed that ISA features improve the performance compared with traditional features, and supervised discriminant techniques outperform in the ISA subspace compared to conventional feature spaces.