Clustering-based two-dimensional linear discriminant analysis for speech recognition

In this paper, a new, Clustering-based Two-Dimensional Linear Discriminant Analysis (Clustering-based 2DLDA) method is proposed for extracting discriminant features in Automatic Speech Recognition (ASR). Based on Two-Dimensional Linear Discriminant Analysis (2DLDA), which works with data represented in matrix space and is adopted to extract discriminant information in a joint spectral-temporal domain, Clustering-based 2DLDA integrates the cluster information in each class by redefining the between-class scatter matrix to tackle the fact that many clusters exist in each state in Hidden Markov Model (HMM)-based ASR. The method was evaluated in the TiDigits connected-digit string recognition and the TIMIT continuous phoneme recognition. Experimental results show that 2DLDA yields a slight improvement on the recognition performance over classical LDA, and our proposed Clustering-based 2DLDA outperforms 2DLDA.