A Comparative Study of Linear and Nonlinear Dimensionality Reduction for Speaker Identification

In this paper we apply linear and nonlinear dimensionality reduction methods to speech produced by a number of different speakers in an effort to yield low dimensional features capable of discriminating between speakers. The classical linear dimensionality reduction method, principal component analysis (PCA), and the nonlinear manifold learning method, Isomap, are investigated. The resulting features are evaluated in GMM-based speaker identification experiments and compared to conventional cepstral features. Isomap is shown to give the highest accuracy for very low dimensions, outperforming MFCCs and PCA transformed features. Isomap is shown to be useful for visualisation of speaker clusters. For higher dimensions, speaker identification results indicate that features resulting from PCA offer improvements over conventional MFCCs.