Dimensionality reduction using MCE-optimized LDA transformation

XiaoBing Li, Jinyu Li, and RenHua Wang

Abstract

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.

Details

Publication typeInproceedings
Published inProc. ICASSP
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