Mixture density estimators in Viterbi training

In speech recognition, speech units are usually modeled as hidden Markov processes. The use of Gaussian mixture densities for computing the local emission probabilities has led to a significant performance improvement. Analytical estimators for updating the parameters in the iterative training are well known if the Baum Welch algorithm is used, but, unfortunately, analytical formulas in the Viterbi case cannot be derived by a limiting process from the Baum Welch formulas. Approximate techniques are used with fairly good success. The authors propose analytical estimators in the Viterbi case as a natural extension of similar formulas used for the plain Gaussian density