Investigation of supervised dimensionality reduction methods for phonetic classification
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
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To improve speech recognition performance, a feature transformation based on discriminant analysis has been widely used to reduce redundant dimensions of features. Linear discriminant analysis (LDA) and Heteroscedastic discriminant analysis (HDA) are often used for this purpose, and a generalization method for LDA and HDA called Power LDA (PLDA) has been proposed. However, these methods may result in unexpected dimensionality reduction for multimodal data. It is important to preserve the local structure of the data in reducing the dimensionality of multimodal data. In this paper we introduce two methods, locality preserving HDA and locality preserving PLDA. We also give an efficient calculation scheme to obtain an optimal projection.