Classification and clustering of stop consonants via nonparametric transformations and wavelets
Basilis Gidas, Alejandro Murua · 2002
We propose a new algorithmic method for the classification and clustering of the six English stop consonants /p, t, k, b, d, g/, on the basis of CV (Consonant-Vowel) or VC syllables data. The method explores two powerful tools: (1) a wavelet representation of the acoustic signal and its induced "waveletogram", a time domain analogue of the spectrogram; (2) nonparametric transformations of the "waveletogram" and a nonlinear discriminant analysis based on these transformations. The procedure has yielded better rates of correct classification than previous methods. Moreover, it yields interesting two-dimensional clustering plots for stop consonants as well as for vowels. The clustering plots for vowels are as separating as those based on the first and second formants; we know of no other method in the literature that yields clustering plots for consonants.