Experiments in dynamic programming inference of Markov networks with strings representing speech data

摘要

Experiments with a method for inference of Markov networks are described. Dynamic programming is used to search for string alignments; which cause high probability, landmark substrings to emerge by reinforcement as the training samples are processed. Network entropy and divergence values are interpreted with respect to the results obtained when inferred networks are used in classification experiments. The data used here are representations of isolated, spoken words mapped into finite strings of symbols.