Frame discrimination training for HMMs for large vocabulary speech recognition

This paper describes the application of a discriminative HMM parameter estimation technique called frame discrimination (FD), to medium and large vocabulary continuous speech recognition. Previous work has shown that FD training can give better results than maximum mutual information (MMI) training for small tasks. The use of FD for much larger tasks required the development of a technique to be able to rapidly find the most likely set of Gaussians for each frame in the system. Experiments on the resource management and North American business tasks show that FD training can give comparable improvements to MMI, but is less computationally intensive.