GMM based on local PCA for speaker identification

Abstract

An efficient Gaussian mixture modelling (GMM) method based on local principal component analysis (PCA) with vector quantisation (VQ) for speaker identification is proposed. The proposed method firstly partitions the data space into several disjoint regions by VQ, and then performs PCA in each region. Finally, the GMM for the speaker is obtained from the transformed feature vectors in each region. Compared to the conventional GMM method with diagonal covariance matrix, under the same performance, the proposed method requires less storage and shows faster results.