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In the past several decades, classifier-independent front-end feature extraction, where the derivation of acoustic features is lightly associated with the back-end model training or classification, has been prominently used in various pattern recognition tasks, including automatic speech recognition (ASR). In this paper, we present a novel discriminative feature transformation, named generalized likelihood...
Linear discriminant analysis (LDA) is designed to seek a linear transformation that projects a data set into a lower-dimensional feature space while retaining geometrical class separability. However, LDA cannot always guarantee better classification accuracy. One of the possible reasons lies in that its formulation is not directly associated with the classification error rate, so that it is not necessarily...
Linear discriminant analysis (LDA) is designed to seek a linear transformation that projects a data set into a lower-dimensional feature space for maximum class geometrical separability. LDA cannot always guarantee better classification accuracy, since its formulation is not in light of the properties of the classifiers, such as the automatic speech recognizer (ASR). In this paper, the relationship...
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