AR modeling of the speech autocorrelation to improve noisy speech recognition
Tipo de documentoTexto en actas de congreso
Fecha de publicación1992
Condiciones de accesoAcceso abierto
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.
CitaciónHernando, J., Nadeu, C. AR modeling of the speech autocorrelation to improve noisy speech recognition. A: Workshop on Speech Processing in Adverse Conditions. "Speech processing in adverse conditions: proceedings: Cannes-Mandelieu (France): 10-13 November 1992". Cannes-Mandelieu: 1992, p. 107-110.