Connectionist Optimisation of Tied Mixture Hidden Markov Models
Steve J. Renals, Nelson H. Morgan, Hervé A. Bourlard, Horacio Franco, Michael M. Cohen · Neural Information Processing Systems · 1991
Issues relating to the estimation of hidden Markov model (HMM) local probabilities are discussed. In particular we note the isomorphism of radial basis functions (RBF) networks to tied mixture density modelling; additionally we highlight the differences between these methods arising from the different training criteria employed. We present a method in which connectionist training can be modified to resolve these differences and discuss some preliminary experiments. Finally, we discuss some outstanding problems with discriminative training.