Comparative analysis for data-driven temporal filters obtained via principal component analysis (PCA) and linear discriminant analysis (LDA) in speech recognition

The Linear Discriminant Analysis (LDA) has been widely used to derive the data-driven temporal filtering of speech feature vectors. In this paper, we proposed that the Principal Component Analysis (PCA) can also be used in the optimization process just as LDA to obtain the temporal filters, and detailed comparative analysis between these two approaches are presented and discussed. It's found that the PCA-derived temporal filters significantly improve the recognition performance of the original MFCC features as LDA-derived filters do. Also, while PCA/LDA filters are combined with the conventional temporal filters, RASTA or CMS, the recognition performance will be further improved regardless the training and testing environments are matched or mismatched, compressed or noise corrupted.