Optimal feature sub-space selection based on discriminant analysis

The performance of a speech recogniser, or of any other pattern classifier, strongly depends on the input features: to obtain a good performance, the feature set needs to be both highly discriminative and compact. Linear discriminant analysis (LDA) is a common data-driven method used to find linear transformations that map large feature vectors onto smaller ones while retaining most of the discriminative power. LDA however oversimplifies the problem by condensing all class information into only two scatter matrices, hence losing important information on the individual class distributions. We therefore propose a new approach, based on the mutual information or minimum classification error paradigm, which takes all information on the individual class distributions into account while searching an optimal sub-space, thus avoiding the crude approximations done by LDA. Experiments show that the proposed scheme provides more discriminative feature vectors, leading to substantially better recognition results.