CLSP Research Note No. 54 Multiple Heteroscedastic Linear Discriminant Analysis
نویسندگان
چکیده
منابع مشابه
Heteroscedastic linear feature extraction based on sufficiency conditions
Classification of high-dimensional data typically requires extraction of discriminant features. This paper proposes a linear feature extractor, called whitened linear sufficient statistic (WLSS), which is based on the sufficiency conditions for heteroscedastic Gaussian distributions. WLSS approximates, in the least squares sense, an operator providing a sufficient statistic. The proposed method...
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To precisely model the time dependency of features is one of the important issues for speech recognition. Segmental unit input HMM with a dimensionality reduction method has been widely used to address this issue. Linear discriminant analysis (LDA) and heteroscedastic extensions, e.g., heteroscedastic linear discriminant analysis (HLDA) or heteroscedastic discriminant analysis (HDA), are popula...
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Discriminant feature spaces are attractive way to improve the word error rate performance of the speech recognition systems. Heteroscedastic discriminant analysis (HDA) is a generalized method for the feature space transformation that does not impose the equa l w i th in c l a s s cova r i ance assumptions required by the standard linear discriminant analysis (LDA). It will be shown that the co...
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تاریخ انتشار 2007