Session 2: Language Modeling

Xuedong Huang

Abstract: This session presented four interesting papers on statistical language modeling aimed for improved large-vocabulary speech recognition. The basic problem in language modeling is to derive accurate underlying representations from a large amount of training data, which shares the same fundamental problem as acoustic modeling. As demonstrated in this session, many techniques used for acoustic modeling can be well extended to deal with problems in language modeling or vice versa. One of the most important issues is how to make effective use of training data to characterize and exploit regularities in natural languages. This is the common theme of four papers presented here.