Generalization of Linear Discriminant Analysis used in Segmental Unit Input HMM for Speech Recognition

To precisely model the time dependency of features is one of the important issues for speech recognition. Segmental unit input HMM with a dimensionality reduction method is widely used to address this issue. Linear discriminant analysis (LDA) and heteroscedastic discriminant analysis (HDA) are classical and popular approaches to reduce dimensionality. However, it is difficult to find one particular criterion suitable for any kind of data set in carrying out dimensionality reduction while preserving discriminative information. In this paper, we propose a new framework which we call power linear discriminant analysis (PLDA). PLDA can describe various criteria including LDA and HDA with one parameter. Experimental results show that the PLDA is more effective than PCA, LDA, and HDA for various data sets.