Automatic V/UV/S classification of continuous speech without a predetermined training set

In speech analysis, a Voiced-Unvoiced-Silence (V/UV/S) decision is performed through pattern recognition, based on measurements made on the signal. The examined speech segment is assigned to a particular class, V/UV/S, based on a minimum probability-of-error decision rule which is obtained under the assumption that the measured parameters are distributed according to a multidimensional Gaussian probability density function. The means and covariances for the Gaussian distribution are determined from manually classified speech data included in a training set. If the recording conditions vary considerably, a new set of training data is required. With the assumption that all three classes exist in the incoming speech signal, this research describes an automatic parametric learning method. Such a method estimates the means and covariances from the incoming speech signal and provides a reliable classification in any reasonable acoustic environment. This approach eliminates the necessity for the manual classification of training data and has the capability of being self-adapting to the background acoustic environment as well as to speech level variations. Thus the presented approach can be readily applied to on-line continuous speech classification without prior recognition.