A Bayesian-adaptive decision method for Voiced-Unvoiced-Silence classification of segments of a speech signal

A method for voiced (V), unvoiced (UV), or silence (S) classification of speech segments, based on the maximum a posteriori probability criterion, is presented. The a posteriori probabilities of the three classes are determined using a vector x equals (f//1 . . . , f//L ) of measurements on the segment under consideration. It is assumed that the vector x has an L-dimensional Gaussian distribution with an expected random value also characterized by an L-dimensional Gaussian distribution. In addition, it is assumed that the sequence of the classes constitutes a first-order stationary Markov chain. The initial parameters are estimated in a training phase. During the application phase, the decision method is adapted by using the previous classifications in order to update the probability density function of the expected random values