Optimising hidden Markov models using discriminative output distributions

Models similar to Doddington's (1989, 1990) hidden Markov models (HMMs) that use phonetically sensitive discriminants are discussed. In this style of HMM, each state models a subspace of the overall acoustic vector; the subspace is chosen to increase discrimination between the in-class and potentially confusable out-of-class utterances. The theoretical basis is presented and various aspects of using these models are discussed, such as the method of gathering confusion statistics; obtaining the correct normalization for the subspace Gaussian distribution and the effects of this term; and the computational requirements for the method. A large number of experiments on a 104 talker British English E-set database were performed that illustrate the utility of the method on a difficult speech recognition task. The experiments give a best speaker-independent error rate 7.9%, and a best multiple speaker error rate of 3.8%.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>