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Spectral moments have been shown to be effective in deriving acoustic features for classifying voiceless stop release bursts [K. Forrest, G. Weismer, P. Milenkovic, and R. N. Dougall, J. Acoust. Soc. Am. 84, 115–123 (1988)]. In this study, we compared the classification of stops /p/, /t/, and /k/ based on spectral moments with classification based on an equal number of Bark cepstrum coefficients. The speech tokens were 446 instances each of utterance‐initial /p/, /t/, and /k/ sampled from utterances produced by 208 children 6 to 8 years old. Linear discriminant analysis (LDA) was used to classify the three stops based on four analysis frames from the initial 40 ms of each token. The best classification based on spectral moments used all four spectral moment features and all four time intervals and yielded 75.6% correct classification. The best classification based on Bark cepstrum yielded 83.4% correct also using four coefficients and four time frames. Differences between these results and previous classi...

Spectral moments versus Bark cepstrum classification of children’s voiceless stops