Indicator variable dependent output probability modelling via continuous posterior functions

This paper investigates the problem of inserting an additional hidden variable into a standard HMM. It is shown that this can be done by introducing a continuous feature which is used to calculate the probability of observing the different states of the hidden variable. The posteriors are modelled by softmax functions with polynomial exponents and an efficient method is developed for reestimating their parameters. After analysing a two dimensional reestimation example on artificial data, the proposed HMM is evaluated on the 1997 Broadcast News task with a particular focus on spontaneous speech. To derive a good indicator variable for this purpose, classification experiments are carried out on fast and slow classes of phones on the 1997 Broadcast News training data. Finally, recognition experiments on the test set of this task show that the proposed model gives an improvement over a standard HMM with a comparable number of parameters.