Estimating the Statistics in Hidden Markov Word Models When the Observations Are Correlated in Time
Original Publication Date: 1987-Jan-01
Included in the Prior Art Database: 2005-Jan-31
In a speech recognition system using Markov models, each Markov model is characterized by transition probabilities and label output probabilities. That is, during a training session, a probability is computed for the occurrence of each transition in each Markov model and, for a label output at a given transition, a respective probability is computed. To reduce the number of probabilities in the models and thereby simplify calculations, it is assumed that the probability of a particular label output being next is dependent on model state and not on previous labels, although there is, in fact, a correlation of label outputs over time. To account for adverse effects resulting from the use of the simplifying assumption, the present invention treats each observed label output (which is a scalar) as a probabilistic vector of labels.