ISCA Archive ICSLP 2000
ISCA Archive ICSLP 2000

Rapid adaptation of n-gram language models using inter-word correlation for speech recognition

Koki Sasaki, Hui Jiang, Keikichi Hirose

In this paper, we study the fast adaptation problem of n-gram language model under the MAP estimation framework. We have proposed a heuristic method to explore inter-word correlation to accelerate MAP adaptation of n-gram model. According to their correlations, the occurrence of one word can be used to predict all other words in adaptation text. In this way, a large n-gram model can be efficiently adapted with a small amount of adaptation data. The proposed fast adaptation approach is evaluated in a Japanese newspaper corpus. We have observed a significant perplexity reduction even when we have only several hundred adaptation sentences.


Cite as: Sasaki, K., Jiang, H., Hirose, K. (2000) Rapid adaptation of n-gram language models using inter-word correlation for speech recognition. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 4, 508-511

@inproceedings{sasaki00_icslp,
  author={Koki Sasaki and Hui Jiang and Keikichi Hirose},
  title={{Rapid adaptation of n-gram language models using inter-word correlation for speech recognition}},
  year=2000,
  booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
  pages={vol. 4, 508-511}
}