Comparing and Evaluating HMM Ensemble Training Algorithms Using Train and Test and Condition Number Criteria.

Hidden Markov Models have many applications in signal processing and pattern recognition, but their convergence-based training algorithms are known to suffer from over-sensitivity to the initial random model choice. This paper describes the boundary between regions in which ensemble learning is superior to Rabiner’s multiple-sequence Baum-Welch training method, and proposes techniques for determining the best method in any arbitrary situation. It also studies the suitability of the training methods using the condition number, a recently proposed diagnostic tool for test-ing the quality of the model. A new method for training Hidden Markov Models Correspondence to