Autoregressive models for noisy speech signals

Linear prediction is a well extended technique for transmission, synthesis and recognition. However when the signal is corrupted by noise, the estimation of the auto-regressive model is known to be biaised. This paper is devoted to methods allowing a reduction of this bias. We will consider first a global method, in which the Yule Walker equations are modified to take into account the variance of an additive white noise. The problem becomes non-linear and is solved recursively. In a second approach, we will examine a time - recursive method based on Kalman filtering.