By Topic

Speech recognition experiments using multi-span statistical language models

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

1 Author(s)
J. R. Bellegarda ; Spoken Language Group, Apple Comput. Inc., Cupertino, CA, USA

A multi-span framework was proposed to integrate the various constraints, both local and global, that are present in the language. In this approach, local constraints are captured via n-gram language modeling, while global constraints are taken into account through the use of latent semantic analysis. The performance of the resulting multi-span language models, as measured by the perplexity, has been shown to compare favorably with the corresponding n-gram performance. This paper reports on actual speech recognition experiments, and shows that word error rate is also substantially reduced. On a subset of the Wall Street Journal speaker-independent, 20,000-word vocabulary, continuous speech task, the multi-span framework resulted in a reduction in average word error rate of up to 17%

Published in:

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on  (Volume:2 )

Date of Conference:

15-19 Mar 1999