Review on Heteroscedastic Discriminant Analysis
نویسنده
چکیده
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 combination of HDA with the maximum likelihood linear transformation (MLLT) leads to the increased classification accuracy even though HDA alone actually degrades word recognition performance. Theoretical review of the mentioned techniques will be provided and contribution of the reference and the reviewed article will be evaluated in this paper.
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