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

Robust speech recognition using neural networks and 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

3 Author(s)
Lin Cong ; Adv. Micro Devices, Santa Clara, CA, USA ; Asghar, S. ; Bin Cong

This paper proposes a robust, speaker-independent isolated word speech recognition (IWSR) system (SMQ/HMM-SVQ/HMM)/MLP which combines dual split matrix quantization (SMQ) and split vector quantization (SVQ) pair combined with both the strength of the HMM in modeling stochastic sequences and the non-linear classification capability of MLP neural networks (NN). The system efficiently utilizes processing resources and improves speech recognition performance by using neural networks as the classifier of the system. Computer simulation clearly indicates the superiority over conventional VQ/HMM and MQ/HMM systems with 98% and 95.8% recognition accuracy at 20 dB and 5 dB SNR levels, respectively in a car noise environment, based on the TIDIGIT database

Published in:

Information Technology: Coding and Computing, 2000. Proceedings. International Conference on

Date of Conference:

2000

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.