5th International Conference on Spoken Language Processing

Sydney, Australia
November 30 - December 4, 1998

Nonlinear Interpolation of Topic Models for Language Model Adaptation

Kristie Seymore, Stanley Chen, Ronald Rosenfeld

Carnegie Mellon University, USA

Topic adaptation for language modeling is concerned with adjusting the probabilities in a language model to better reflect the expected frequencies of topical words for a new document. We present a novel technique for adapting a language model to the topic of a document, using a nonlinear interpolation of n-gram language models. A three-way, mutually exclusive division of the vocabulary into general, on-topic and off-topic word classes is used to combine word predictions from a topic-specific and a general language model. We achieve a slight decrease in perplexity and speech recognition word error rate on a Broadcast News test set using these techniques. Our results are compared to results obtained through linear interpolation of topic models.

Full Paper

Bibliographic reference.  Seymore, Kristie / Chen, Stanley / Rosenfeld, Ronald (1998): "Nonlinear interpolation of topic models for language model adaptation", In ICSLP-1998, paper 0897.