On the use of bandpass liftering in speech recognition

In this paper, we extend the interpretation of distortion measures, based upon the observation that measurements of speech spectral envelopes (as normally obtained from analysis procedures) are prone to statistical variations due to window position fluctuations, excitation interference, measurement noise, etc. and may possess spurious characteristics because of analysis model constraints. We have found that these undesirable spectral measurement variations can be controlled (i.e. reduced in the level of variation) through proper cepstral processing and that a statistical model can be established to predict the variances of the cepstral coefficient measurements. The findings lead to the use of a bandpass "liftering" process aimed at reducing the variability of the statistical components of spectral measurements. We have applied this liftering process to various speech recognition problems; in particular, vowel recognition and isolated word recognition. With the liftering process, we have been able to achieve an average digit error rate of 1%, which is about half of the previously reported best results, with dynamic time warping in a speaker-independent isolated digit test.