A study of using locality preserving projections for feature extraction in speech recognition

This paper presents a new approach to feature analysis in automatic speech recognition (ASR) based on locality preserving projections (LPP). LPP is a manifold based dimensionality reduction algorithm which can be trained and applied as a linear projection to ASR features. Conventional manifold based dimensionality reduction algorithms are generally restricted to batch mode implementation and it is difficult in practice to apply them to unseen data. It is argued that LPP can model feature vectors that are assumed to lie on a nonlinear embedding subspace by preserving local relations among input features, so it has a potential advantage over conventional linear dimensionality reduction algorithms like principal components analysis (PCA) and linear discriminant analysis (LDA). Experimental results obtained on the Resource Management (RM) data set showed that when LPP based dimensionality reduction was applied in the context of mel frequency cepstrum coefficient (MFCC) based feature analysis, a significant reduction of word error rate (WER) was obtained with respect to standard MFCC features.