By Topic

Out-of-vocabulary word modeling using multiple lexical fillers

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

2 Author(s)
G. Boulianne ; Centre de recherche informatique de Montreal, Que., Canada ; P. Dumouchel

In large vocabulary speech recognition, out-of-vocabulary words are an important cause of errors. We describe a lexical filler model that can be used in a single pass recognition system to detect out-of-vocabulary words and reduce the error rate. When rescoring word graphs with better acoustic models, word fillers cause a combinatorial explosion. We introduce a new technique, using several thousand lexical fillers, which produces word graphs that can be rescored efficiently. On a large French vocabulary continuous speech recognition task, lexical fillers achieved an OOV detection rate of 44% and allowed a 23% reduction in errors due to OOV words.

Published in:

Automatic Speech Recognition and Understanding, 2001. ASRU '01. IEEE Workshop on

Date of Conference: