A dynamic cepstrum incorporating time-frequency masking and its application to continuous speech recognition

Abstract

A dynamic cepstrum parameter that incorporates the time-frequency characteristics of auditory forward masking is proposed. A masking model is derived from psychological experimental results. A novel operational method using a lifter array is derived to perform the time-frequency masking. The parameter simulates the effective input spectrum at the front-end of the auditory system and can enhance the spectral dynamics. The parameter represents both the instantaneous and transitional aspects of a spectral time series. Phoneme and continuous speech recognition experiments demonstrated that the dynamic cepstrum outperforms the conventional cepstrum individually and in various combinations with other spectral parameters. The phoneme recognition results were improved for ten male and ten female speakers. The masking lifter with a Gaussian window provided a better performance than that with a square window.<>