Discriminative analysis for feature reduction in automatic speech recognition

A dimensionality reduction method of the frame feature space based on discriminative analysis is discussed. A significant dimensionality reduction is obtained without loss of recognition performance in speaker independent experiments on a variety of speech databases. In addition, this procedure allows the selective incorporation of new feature components into an existing feature set.<<ETX>>

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