Lattice-based discriminative training for large vocabulary speech recognition

This paper describes a framework for optimising the parameters of a continuous density HMM-based large vocabulary recognition system using a maximum mutual information estimation (MMIE) criterion. To limit the computational complexity arising from the need to find confusable speech segments in the large search space of alternative utterance hypotheses, word lattices generated from the training data are used. Experiments are presented on the Wall Street journal database using up to 66 hours of training data. These show that lattices combined with an improved estimation algorithm makes MMIE training practicable even for very complex recognition systems and large training sets. Furthermore, experimental results show that MMIE training can yield useful increases in recognition accuracy.