September 22-25, 1997
In this paper, a new approach for linear prediction (LP) analysis is proposed. This approach makes the assumption that the speech signal is cyclostationary and uses cyclic autocorrelation function for computing LP parameters. Since the cyclic autocorrelation function of a stationary random signal is zero, independent of its statistical description, this analysis is robust to additive noise, white or colored. It is applied to speech recognition. Preliminary results demonstrate its robustness to white additive noise.
Bibliographic reference. Paliwal, Kuldip K. / Sagisaka, Yoshinori (1997): "Cyclic autocorrelation-based linear prediction analysis of speech", In EUROSPEECH-1997, 279-282.