Selection of the best wavelet packet nodes based on mutual information for speaker identification

Abstract

The analysis of the speech signal using wavelet packet trees (WPT) is a very flexible tool, capable of effectively manipulate the frequency subbands thanks to the orthonormal bases it provides. Here, dimension reduction becomes very important since the number of subbands grows exponentially with the level of decomposition, and their discriminative relevancy is different, which leads to different resolution for each one. A method based on mutual information is proposed in order to keep as much discriminative information as possible and the less amount of redundant information. © 2008 Springer-Verlag Berlin Heidelberg.