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

Speech recognition using sub-word neural tree network models and multiple classifier fusion

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)
Sharma, M. ; CAIP Center, Rutgers Univ., Piscataway, NJ, USA ; Mammone, R.

A new neural tree network (NTN)-based speech recognition system is presented. NTN is a hierarchial classifier that combines the properties of decision trees and feed-forward neural networks. In the sub-word unit-based system, the NTNs model the sub-word speech segments, while the Viterbi algorithm is used for temporal alignment. Durational probability is associated with each sub-word NTN. An iterative algorithm is proposed for training the sub-word NTNs. The sub-word NTN models, as well as the subword segment boundaries within a vocabulary word, are re-estimated. Thus, the proposed system is a homogeneous neural network-based, sub-word unit-based, speech recognition system. Furthermore, embedded within this word model paradigm, multiple NTNs are trained for each subword segment and their output decisions are combined or fused to yield improved performance. The proposed discriminatory training-based system did not perform favourably as compared to a hidden Markov model-based system. The paradigm presented in this paper can be argued to represent a class of discriminatory training-based, homogeneous (versus hybrid), sub-word unit-based, speech recognition systems. Hence, the results reported here can be generalized to other similar systems

Published in:

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on  (Volume:5 )

Date of Conference:

9-12 May 1995

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.