Classification of pitch periods using expert knowledge and neural net classifiers

Abstract

A pitch tracking algorithm that uses an artificial neural net (ANN) classifier to identify pitch periods has been developed. The algorithm consists of a peak detector that locates candidate peaks in the filtered waveform (0–700 Hz), and an ANN classifier that uses feature measurements to decide if the candidate peak begins a pitch period. The feature measurements include (a) the amplitude of the candidate peak, (b) the amplitude of the 7 peaks before and the 7 peaks after the candidate peak, (c) the location of these 14 peaks relative to the candidate peak, and (d) the median pitch observed so far in the utterance, based on the prior output of the classifier. The classifier was trained and evaluated on hand‐labeled pitch periods in utterances in the DARPA TIMIT database. The classifier contained one hidden layer with six units, and was trained using backpropagation. Initial results on 10 test utterances using small amounts of training data revealed 98% correct classification of candidate peaks. Results of subsequent experiments will be presented.