Cart (Loading....) | Create Account
Close category search window
 

Topic n-gram count language model adaptation for speech recognition

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

2 Author(s)
Haidar, M.A. ; INRS-EMT, Montreal, QC, Canada ; O'Shaughnessy, D.

We introduce novel language model (LM) adaptation approaches using the latent Dirichlet allocation (LDA) model. Observed n-grams in the training set are assigned to topics using soft and hard clustering. In soft clustering, each n-gram is assigned to topics such that the total count of that n-gram for all topics is equal to the global count of that n-gram in the training set. Here, the normalized topic weights of the n-gram are multiplied by the global n-gram count to form the topic n-gram count for the respective topics. In hard clustering, each n-gram is assigned to a single topic with the maximum fraction of the global n-gram count for the corresponding topic. Here, the topic is selected using the maximum topic weight for the n-gram. The topic n-gram count LMs are created using the respective topic n-gram counts and adapted by using the topic weights of a development test set. We compute the average of the confidence measures: the probability of word given topic and the probability of topic given word. The average is taken over the words in the n-grams and the development test set to form the topic weights of the n-grams and the development test set respectively. Our approaches show better performance over some traditional approaches using the WSJ corpus.

Published in:

Spoken Language Technology Workshop (SLT), 2012 IEEE

Date of Conference:

2-5 Dec. 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.