Dimensionality reduction using MCE-optimized LDA transformation

In this paper, Minimum Classification Error (MCE) method is
extended to optimize both Linear Discriminant Analysis (LDA)
transformation and the classification parameters for
dimensionality reduction. Firstly, under the HMM-based
Continuous Speech Recognition (CSR) framework, we use
MCE criterion to optimize the conventional dimensionality
reduction method, which uses LDA to transform standard
MFCCs. Then, a new dimensionality reduction method is
proposed. In the new method, the combination of Discrete
Cosine Transform (DCT) and LDA, as used in the conventional
method, is replaced by a single LDA transformation, which is
optimized according to MCE criterion along with the
classification parameters. Experimental results on TiDigits
show that even when the feature dimension is reduced to 14, the
performance of this new method is as good as that of the MCEtrained
system using 39 dimension MFCCs. It also outperforms
our MCE-optimized conventional dimensionality reduction
method.