Quasi-Newton method for maximum likelihood estimation of hidden Markov models

Hidden Markov models (HMMs) are used in many signal processing applications including speech recognition, blind equalization of digital communications channels, etc. The most widely used method for maximum likelihood estimation of HMM parameters is the forward-backward (or Baum-Welch) algorithm which is an early example of application of the expectation-maximization (EM) principle. In this contribution, an alternative fast-converging approach for maximum likelihood estimation of HMM parameters is described. This new techniques is based on the use of general purpose quasi-Newton optimization methods as well as on an efficient purely recursive algorithm for computing the log-likelihood and its derivative