Automatic extraction of stop-oriented features from Chinese speech wave using wavelet transform

A novel method for automatically extracting stop-oriented features from Chinese speech using wavelet transforms is presented. This method classifies Chinese voiceless consonants into two stop subsets, BDG: {b,d,g} and zZHJGPTcCHQK: {z,zh,j,g,p,t,c,ch,q,k}, and one fricative subset FsSHhX: {f,s,sh,x,h}. For each speech token of C^-V syllables (V^- denotes voiceless consonant), the algorithm calculates detection objectives and outputs boundary marks of the voiceless consonants and one of symbols out of {b.d.g, STOP/BD, f.s.sh.x.h} as its category markers. The validity of the method was tested on a subset of 913 C^-V syllables extracted from a database consisting of 1276 Chinese all-syllable tokens, with hand-labeled initial and final segment markers as the benchmark of the test, resulting in a classification accuracy of 96.1%, 95.1%, and 89.0% for category b.d.g, STOP/BD, and f.s.sh.x.h respectively, and in an average accuracy of 93.6% for all of the 913 C^-V syllables