Knowledge‐based recognition of voiceless stop consonants

Abstract

Recent experiments [M. Bush et al., Proc. 1983 IEEE ICASSP] have demonstrated the ability of a trained spectrogram reader to identify initial stops in /CVb/ syllables from a table of numerical acoustic measurements with approximately 80% accuracy. This paper discusses an automatic system for discriminating between the voiceless plosives (/p,t,k/)which is based on the features and rules identified in these experiments. Ten binary features are extracted from two linear prediction spectra which are computed during the 35 ms following the consonant release. Typical features include “back‐k‐release‐spectrum” and “compact‐release‐spectrum.” The features are detected by examining the frequencies and amplitudes of the local maxima and minima of the two LPC spectra, in a manner motivted by the actions of the human spectrogram reader. A simple statistical classifier is used to combine the outputs of the ten feature detectors. The classifier was trained on the 108 /p,t,k/ tokens of the multi‐speaker corpus used in the spectrogram reading experiment. When tested on the training data, the system achieved 96% correct recognition. When tested on two additional data sets of similar composition, the system achieved scores of 94% and 92%, respectively.