Speaker identification in noisy conditions using linear prediction of one-sided autocorrelation sequence
Francisco Javier Hernando Pericás, Climent Nadeu Camprubí, C. Villagrasa, Enric Monte · International Conference on Spoken Language Processing · 2004
The OSALPC (One-Sided Autocorrelation Linear Predictive Coding) representation of the speech signal has shown to be attractive for speech recognition because of its simplicity and its high recognition performance with respect to the standard LPC in severe noisy conditions. In this paper the OSALPC technique is applied to the problem of speaker identification in noisy conditions. As shown with experimental results, using additive white noise, that technique also achieves much better results than both LPC and mel-cepstrum parameterizations in this task.