Multitapering and a wavelet variant of MFCC in speech recognition

摘要

In speech recognition (ASR) based on hidden Markov models (HMM) it is necessary to obtain a spectral approximation with a reduced set of representation coefficients. The author introduces to the speech parameterisation scheme multitapering and a modification of the usual mel frequency cepstrum coefficients (MFCC) processing scheme based on wavelets on intervals (wavelet frequency coefficients, WFC). Phoneme recognition performance improvements compared to the MFCC have been experimentally verified on data from a speech database, using multitapering and WFC.