Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. For technical support, please contact us at onlinesupport@ieee.org. We apologize for any inconvenience.
By Topic

Training Wideband Acoustic Models Using Mixed-Bandwidth Training Data 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)
Seltzer, M.L. ; Microsoft Res., Redmond, WA ; Acero, A.

One serious difficulty in the deployment of wideband speech recognition systems for new tasks is the expense in both time and cost of obtaining sufficient training data. A more economical approach is to collect telephone speech and then restrict the application to operate at the telephone bandwidth. However, this generally results in suboptimal performance compared to a wideband recognition system. In this paper, we propose a novel expectation-maximization (EM) algorithm in which wideband acoustic models are trained using a small amount of wideband speech and a larger amount of narrowband speech. We show how this algorithm can be incorporated into the existing training schemes of hidden Markov model (HMM) speech recognizers. Experiments performed using wideband speech and telephone speech demonstrate that the proposed mixed-bandwidth training algorithm results in significant improvements in recognition accuracy over conventional training strategies when the amount of wideband data is limited

Published in:

Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:15 ,  Issue: 1 )