Minimum mean square error estimation of speech short-term predictor parameters under noisy conditions

Minimum mean square error (MMSE) estimation of the speech short-term predictor (STP) parameters in the line spectral frequency (LSF) representation is considered. We exploit that the square error between LSF parameter vectors is a subjectively meaningful distortion criterion. As speech coding algorithms are often used in a noisy environment, it is relevant to estimate the STP parameters used in these algorithms under the inclusion of noise statistics. In our experiments, car noise is used as an example of an autoregressive (AR) noise process. The MMSE estimates are obtained using a likelihood function computed by means of Kalman filtering and empirical probability distributions. The method is assessed in terms of the resulting root mean spectral distortion between the ´clean´ speech STP parameters and the STP parameters computed using the proposed method from noisy speech.