Hidden Markov models (HMM’s) are popular in many applications, such as automatic speech recognition, control theory, biology, communication theory over channels with bursts of errors, queueing theory, and many others. Therefore, it is important to have robust and fast methods for fitting HMM’s to experimental data (training). Standard statistical methods of maximum likelihood parameter estimati...