Data-driven temporal filters based on multi-eigenvectors for robust features in speech recognition

It was previously proposed to use the principal component analysis (PCA) to derive the data-driven temporal filters for obtaining robust features in speech recognition, in which the first principal components are taken as the filter coefficients. In this paper, a multi-eigenvector approach is proposed instead, in which the first M eigenvectors obtained in PCA are weighted by their corresponding eigenvalues and summed to be used as the filter coefficients. Experimental results showed that the multi-eigenvector filters offer significant recognition performance as compared to the previously proposed PCA-derived filters under all different conditions tested with the AURORA2 database, especially when the training and testing environments are highly mismatched.