A statistical procedure for classifying word‐initial voiceless obstruents is described. The data set to which the analysis was applied consisted of monosyllabic words starting with a voiceless obstruent. Each word was repeated six times in the carrier phrase ‘‘I can say —— , again’’ by each of ten speakers. Fast Fourier transforms (FFTs), using a 20‐ms Hamming window, were calculated every 10 ms from the onset of the obstruent through the third cycle of the following vowel. Each FFT was treated as a random probability distribution from which the first four moments (mean, variance, skewness, and kurtosis) were computed. Moments were calculated from linear and Bark transformed spectra. Data were pooled across vowel contexts for speakers of a given gender and input to a discriminant analysis. Using the moments calculated from the linear spectra, 92% of the voiceless stops were classified correctly when dynamic aspects of the stop were included. Even more important, the model constructed from the males’ data correctly classified about 94% of the voiceless stops produced by the female speakers. Classification of the voiceless fricatives when all places of articulation were included in the analysis did not exceed 80% correct when the moments from either the linear or Bark transformed scales were used. However, classification of only the voiceless sibilants was 98% correct when the moments from the Bark transformed spectra were used. As with the stops, the classification model held across gender.

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