Model topology selection for isolated word recognition

The paper describes a search procedure that, given a set of alternate models for each word of a small vocabulary isolated words recognizer, selects the set of models that minimizes the expected number of errors. The reported results show that the number of errors that occur on the test set by using the best set of models selected from the training set is less than the one achieved by models with a fixed number of states, or the same number of errors is obtained with less states