Application of time-frequency principal component analysis to text-independent speaker identification

Abstract

We propose a formalism, called vector filtering of spectral trajectories, that allows the integration of a number of speech parameterization approaches (cepstral analysis, /spl Delta/ and /spl Delta//spl Delta/ parameterizations, auto-regressive vector modeling, ...) under a common formalism. We then propose a new filtering, called contextual principal components (CPC) or time-frequency principal components (TFPC). This filtering consists in extracting the principal components of the contextual covariance matrix, which is the covariance matrix of a sequence of vectors expanded by their context. We apply this new filtering in the framework of closed-set speaker identification, using a subset of the POLYCOST database. When using speaker-dependent TFPC filters, our results show a relative improvement of approximately 20% compared to the use of the classical cepstral coefficients augmented by their /spl Delta/-coefficients, which is significantly better with a 90% confidence level.