Research on speaker feature dimension reduction based on CCA and PCA

A method to reduce feature dimension based on CCA and PCA is proposed. First, using the CCA to fuse the LPC features based on channel model and the MFCC feature based on auditory model to improve the relevance of the two different features; second, utilizing the PCA to further remove redundant features, and reduce the dimension of effective features. To verify the validity of this method, experimental model is based on GMM speaker recognition system, and 16-dimensional LPC and 13-dimensional MFCC are selected as speaker features. Compared with the traditional dimension reduction method, such as CCA, PCA and manual methods, experiments show that CCA+PCA method can further enhance the effect of dimension reduction.