Application of vector filtering to pattern recognition

We present a new formalism, called vector filtering, which consists in transforming a sequence of vectors through a matrical filtering. This formalism allows one to unify a number of classical approaches. We also show how vector filtering can be integrated in a pattern recognition system. We then propose a new filtering, called contextual principal components, which consists in calculating principal components on vectors augmented by their context. Then, we apply the new filtering in the framework of text-independent speaker identification, which consists in identifying a speaker by the voice without knowledge about the phonetic content. By using this new filtering, we are able to decrease the identification error rate to roughly 20 % compared to a system using the classical cepstral coefficients augmented by their delta parameters