Kernel Discriminative Analysis for Speech Recognition
نویسندگان
چکیده
Linear Discriminative Analysis techniques have been used in pattern recognition to map feature vectors to achieve optimal classification. Kernel Discriminative Analysis(KDA) seeks to introduce non-linearity in this approach by mapping the features to a non-linear space before applying LDA analysis. The formulation is expressed as an eigenvalue problem resolution. Using a different kernel, one can cover a wide class of nonlinearities. In this paper, we describe this technique and present an application to a speech recognition problem. We give classification results for a connected digit recognition task and analyze some existing problems.
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