Selection of optimal dimensionality reduction method using chernoff bound for segmental unit input HMM

To precisely model the time dependency of features, segmental unit input HMM with a dimensionality reduction method has been widely used for speech recognition. Linear discriminant analysis (LDA) and heteroscedastic discriminant analysis (HDA) are popular approaches to reduce the dimensionality. We have proposed another dimensionality reduction method called power linear discriminant analysis (PLDA) to select the best dimensionality reduction method that yields the highest recognition performance. This selection process on the basis of trial and error requires much time to train HMMs and to test the recognition performance for each dimensionality reduction method. In this paper we propose a performance comparison method without training or testing. We show that the proposed method using the Chernoff bound can rapidly and accurately evaluate the relative recognition performance.