By Topic

Semantic query expansion and context-based discriminative term modeling for spoken document retrieval

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
$33 $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

4 Author(s)
Tsung-wei Tu ; Graduate Institute of Computer Science and Information Engineering, National Taiwan University, Taiwan ; Hung-yi Lee ; Yu-yu Chou ; Lin-shan Lee

In this paper, we propose a semantic query expansion approach by extending the query-regularized mixture model to include latent topics and apply it to spoken documents. We also propose to use context feature vectors for spoken segments to train SVM models to enhance the posterior-weighted normalized term frequencies in lattices. Experiments on Mandarin broadcast news showed that this approach offered good improvements when applied on spoken documents including relatively high recognition errors.

Published in:

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Date of Conference:

25-30 March 2012