Selecting acoustic features for stop consonant identification

A series of experiments was performed in order to select a set of acoustic measurements for use as input to an expert system for stop consonant recognition. In the experiments, a trained human spectrogram reader made six-way (/b,d,g,p,t,k/) classifications of syllable-initial stops using four different data representations: DFT spectrograms, LPC spectrograms, LPC spectral slices and tables of numerical measurements. Percent correct identification was 79%, 81%, 72% and 76%, respectively, for the four data sets. The relatively high performance achieved using the numerical measurements, together with other considerations for selecting input representations for expert systems, suggest that the numerical tables are the most appropriate of the four forms of input.