Long Span Features and Minimum Phoneme Error Heteroscedastic Linear Discriminant Analysis

In this paper we explore the effect of long-span features, resulting from concatenating multiple speech frames and projecting the resulting vector onto a subspace using Linear Discriminant Analysis (LDA) techniques. We show that LDA is not always effective in selecting the optimal combination of long-span features, and introduce a discriminative feature analysis method that seeks to minimize phoneme errors on training lattices. This technique, referred to as Minimum Phoneme Error Heteroscedastic Linear Discriminant Analysis (MPE-HLDA), is shown to be more robust than LDA when applied to long-span features and easy to incorporate with existing training procedures, such as HLDA-SAT and discriminative training of Hidden Markov Models (HMMs). Results on conversational telephone speech and broadcast news corpora also show that the recognition accuracy is improved using features selected by MPE-HLDA.