A wavelet representation evaluation for stop-consonants classification

Regarding Short Time Fourier Transform based methods, stop-consonants representation could be improved using the wavelet transform. After presenting our framework, we describe the wavelet parameterization and the classification method. Stop consonants are represented with pseudo-cepstral wavelet based parameters computed on a single-burst-neighbourhood-20 ms frame. Non-parametric nearest neighbours method is used. Evaluation is speaker-in dependent; 1593 stop-consonants extracted from T1MIT database are evaluated. Results are described and discussed comparatively to MFCC's (Mel Frequency Cepstrum Coefficients). It appears that, in our field of research, wavelet gives equivalent classification percentages. The first thing which was pointed out, is the necessity to build an elaborated-wavelet-based-representation to get significant improvements.