10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Use of Contexts in Language Model Interpolation and Adaptation

X. Liu, M. J. F. Gales, P. C. Woodland

University of Cambridge, UK

Language models (LMs) are often constructed by building component models on multiple text sources to be combined using global, context free interpolation weights. By re-adjusting these weights, LMs may be adapted to a target domain representing a particular genre, epoch or other higher level attributes. A major limitation with this approach is other factors that determine the “usefulness” of sources on a context dependent basis, such as modeling resolution, generalization, topics and styles, are poorly modeled. To overcome this problem, this paper investigates a context dependent form of LM interpolation and test-time adaptation. Depending on the context, a discrete history weighting function is used to dynamically adjust the contribution from component models. In previous research, it was used primarily for LM adaptation. In this paper, a range of schemes to combine context dependent weights obtained from training and test data to improve LM adaptation are proposed. Consistent perplexity and error rate gains of 6% relative were obtained on a state-of-the-art broadcast recognition task.

Full Paper

Bibliographic reference.  Liu, X. / Gales, M. J. F. / Woodland, P. C. (2009): "Use of contexts in language model interpolation and adaptation", In INTERSPEECH-2009, 360-363.