In this paper, we propose a formalism, called vector filtering of spectral trajectories, which allows to integrate under a common formalism a lot of speech parameterization approaches. We then propose a new filtering in this framework, called time-frequency principal components (TFPC) of speech. We apply this new filtering in the framework of speaker identification, using a subset of the POLYCOST database. The results show an improvement of roughly 20% compared to the use of the classical cepstral coefficients augmented by their Delta-coefficients.