Acoustic Modelling Using Kernel-Based Discriminants
نویسندگان
چکیده
In this paper we use kernel-based Fisher Discriminants (KFD) for classification by integrating this method in a HMM-based speech recognition system. We translate the outputs of the KFD-classifier into conditional probabilities and use them as production probabilities of a HMM-based decoder for speech recognition. To obtain a good performance also in terms of computational complexity the Recursive Least Squares Algorithm (RLS-Algorithm) is enforced. We train and test the described hybrid structure on the Resource Management Corpus (RM1).
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