Bias propagation in the autocorrelation method of linear prediction

Many low bit-rate speech coders use the autocorrelation method (ACM) to find a linear prediction model of the speech signal. A time-domain analysis of the ACM for autoregressive estimation is given. It is shown that a small bias in a reflection coefficient close to one in absolute value is propagated and prohibits an accurate estimation of further reflection coefficients. Tapered data windows largely reduce this effect, but increase the variance of the models