Characteristics of reflection coefficient estimates based on a Markov chain model

A new estimation technique for the reflection coefficients in the lattice filter formulation of linear predictive analysis is presented that is computationally more efficient than previous procedure. This procedure, requiring primarily addition operations, is based on a Markov Chain model of the hard-limited forwards and backwards prediction error sequence. By using only the polarity of the prediction error signal, the reflection coefficient estimates cannot be as accurate as those from optimum estimation techniques. However, the precision of the estimates is comparable to the quantization error of the reflection coefficients in speech encoding and transmission applications. The theoretical foundation of this estimation technique is presented. The estimates from this technique are compared with those from optimal algorithms.