Minimum Phoneme Error Based Heteroscedastic Linear Discriminant Analysis for Speech Recognition
Bing Zhang, Spyros Matsoukas · 2006
We introduce a discriminative feature analysis method that seeks to minimize phoneme errors in lattice-based training frameworks. This technique, referred to as minimum phoneme error heteroscedastic linear discriminant analysis (MPE-HLDA), is shown to be more robust than traditional LDA methods in high dimensional spaces, 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.