Autoregressive parameter estimation of speech in noise

We describe a method for estimating the spectral parameters of speech corrupted by additive noise based on prior statistics of their trajectories. This method uses a two-stage estimation procedure. In the first step, the maximum likelihood estimate of the line spectrum pair frequencies and average power is determined. However, these estimates are known to have an unacceptably large variance and follow unnatural trajectories. To improve these estimates, we propose modeling the spectral parameters with a jump Markov linear system. This model accommodates both the rapid transitions that occur during consonants, and the slowly changing dynamics of vowels. We use this model to derive a new estimator for autoregressive speech parameters that does not introduce delay and compares favorably with the MELPe speech enhancement scheme.