Classification of voiced and unvoiced speech by hierarchical stochastic modeling

In this paper, we consider the classification of speech signals by using stochastic models at different scales. The signal at different scales is modeled by a hierarchical autoregressive moving average (ARMA) model, and the features at coarse scales are extracted from the model without performing expensive filtering operation. The hierarchical modeling can increase the accuracy of speech classification by exploiting features at different scales. For speech classification, model parameters at five different scales obtained by hierarchical modeling are used as features. A minimum distance classifier is implemented, and tested on TIMIT speech data.