Sequential classification of probabilistic independent feature vectors based on multilayer perceptron

Abstract

The paper presents methods of classification based on a sequence of feature vectors extracted from signal generated by the object. The feature vectors are assumed to be probabilistic independent. Each feature vector is separately classified by a multilayer perceptron giving a set of local classification decisions. This set of statistical independent decisions is a base for a global classification rule. The rule is derived from statistical decision theory. According to it, an object belongs to a class for which product of corresponding neural network outputs is the largest. The neural outputs are modified in a way to prevent them vanishing to zero. The performance of the proposed rule was tested in an automatic, text independent, speaker identification task. Achieved results are presented. © Springer-Verlag Berlin Heidelberg 2005.