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A classifier to make the voiced/unvoiced (V/UV) decision in speech analysis which performs with an error rate of less than half of a percent is presented. The decision making process is viewed as a pattern recognition problem in which a number of features can be used to make the classification. Training is accomplished using a nonparametric, nonstatistical technique. In order to obtain a classifier which would make the correct decision for a variety of speakers and to determine which of the features under consideration should be used, a procedure for interleaving the contributions of the feature and speaker sets was developed. This procedure is presented in terms of the notions of covering and satisfaction. The failure of a classifier to cover a set of speakers indicates that more training information from those speakers is necessary to define the classifier. The failure of the classifier to satisfy a set of speakers indicates that the performance of the classifier could be improved by the use of more features in making the V/UV decision. In the training procedure, covering and satisfaction were attained on successively larger sets of speakers, with the result that a classifier was obtained which could correctly make the V/UV decision for all of the speakers used in testing, including those not used in the training process.