Connectionist Optimisation of Tied Mixture Hidden Markov Models

Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)

Bibtex Metadata Paper


Steve Renals, Nelson Morgan, Hervé Bourlard, Horacio Franco, Michael Cohen


Issues relating to the estimation of hidden Markov model (HMM) local probabilities are discussed. In particular we note the isomorphism of ra(cid:173) dial basis functions (RBF) networks to tied mixture density modellingj 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 out(cid:173) standing problems with discriminative training.