Data-driven temporal filters for robust features in speech recognition obtained via Minimum Classification Error (MCE)

In deriving the data-driven temporal filters for speech features, the Linear Discriminant Analysis (LDA) and the Principal Component Analysis (PCA) have been shown to be successful in improving the feature robustness. In this paper, it's proposed that the criterion of Minimum Classification Error (MCE) can also be used to obtain the data-driven temporal filters. Two versions of MCE-derived temporal filters, Feature-based and Model-based, are proposed and it is shown that both of them can significantly improve the recognition performance of the original MFCC features as the LDA/PCA-derived filters do. Detailed comparative analysis among the different temporal filtering approaches is presented. It is also shown that the proposed MCE filters can be integrated with the conventional temporal filters, RASTA or CMS, to obtain improved recognition performance regardless of whether the training and testing environments are matched or mismatched, compressed or noise corrupted.