By Topic

Isolated word recognition using interframe dependent hidden Markov 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
$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)
Ming, J. ; Sch. of Electr. & Comput. Eng., Queen''s Univ., Belfast, UK ; Smith, F.J.

A new hidden Markov model (HMM) with first-order dependent observation densities is presented to account for the statistical dependence between successive frames. In this model, the dependence relation among the frames is optimized to maximize the likelihood for both the training and testing data. Experimental comparisons with the standard continuous density HMM as well as the conditional-observation HMM for an isolated word recognition task show that a significant performance improvement is achieved for the new model.<>

Published in:

Signal Processing Letters, IEEE  (Volume:1 ,  Issue: 12 )