By Topic

Just-in-time latent semantic adaptation on language model for Chinese speech recognition using Web data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Qin Gao ; Speech & Hearing Res. Center, Peking Univ., Beijing ; Xiaojun Lin ; Xihong Wu

A novel method is proposed, which is for performing just-in- time adaptation on language models in Chinese speech recognition using Web search engines. Latent semantic analysis (LSA) is employed to change the probability distribution of N-gram language model. The method has two advantages. First, it needs relatively small amount of data which can be obtained from Web on-the-fly. Second, comparing to traditional adaptation formula of LSA, the proposed approach is more efficient, which ensures second pass decoding to be performed with high speed. Experiments show that the perplexity of language model is reduced by over 13% after adaptation. A 4.29% relative reduction on WER is achieved in large vocabulary Chinese speech recognition over standard test set.

Published in:

Spoken Language Technology Workshop, 2006. IEEE

Date of Conference:

10-13 Dec. 2006