Spectral moment vs. bark cepstral analysis of children's word-initial voiceles stops

Figure 3. Position of each case on linear discriminant 1 (LD1) versus linear discriminant 2 (LD2). Spectral moments analysis has been shown to be effective in deriving acoustic features for classifying voiceless stop release bursts [1], and is an analysis method that has commonly been cited in the clinical phonetics literature dealing with children’s disordered speech. 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. Utterance-initial /p/, /t/, and /k/ (1338 samples in all) were collected from a database of children’s speech. Linear discriminant analysis (LDA) was used to classify the three stops based on four analysis frames from the initial 40 msec of each token. The best model based on spectral moments used RMS amplitude plus all four bark-scaled spectral moment features at all four time intervals and yielded 78.0% correct discrimination. The best model of similar rank based on Bark cepstrum features yielded 86.6% correct segment discrimination.