On the AR modelling of the one-sided autocorrelation sequence for noisy speech recognition
Document typeConference report
PublisherUniversity of Alberta
Rights accessOpen Access
Speech recognition in noisy environments remains an unsolved problem even in the case of isolated word recognition with small vocabularies. Recently, several techniques have been proposed to alleviate this problem. Concretely, two closely related parameterization techniques based on an AR modelling in the autocorrelation domain called SMC  and OSALPC  have shown good results using speech contaminated by additive white noise. The aim of this paper is twofold: to compare several techniques based on an AR modelling in the autocorrelation domain, including SMC and OSALPC, and to find the optimum model order and cepstral liftering for noisy conditions.
CitationHernando, J., Nadeu, C. On the AR modelling of the one-sided autocorrelation sequence for noisy speech recognition. A: International Conference on Spoken Language Processing. "ICSLP 1992: ICSLP 92 proceedings: International Conference on Spoken Language Processing: October 12-16, 1992: Banff, Alberta, Canada". Banff, Alberta: University of Alberta, 1992, p. 1593-1595.