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

A PC-based neural network for recognition of difficult syllables using LPC coefficient difference

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)

The recognition of difficult CV (consonant-vowel) and VC (vowel-consonant) syllables using PC-based neural network and linear predictive coding (LPC) coefficients is studied. The speech corpus consisted of 41 syllables produced by three speakers in three different vowel contexts. The input to the neural network was differences in LPC coefficients sampled at three time instances in each syllable. Fully connected three layered backpropagation networks were trained by the delta learning rule. With relatively few parameters for each syllable, based on 123 tokens of 41 difficult syllables spoken with sentence context by three speakers (one male, two female), the preliminary results indicate that recognition accuracy is as high as 92.7%

Published in:

Neural Networks, 1990., 1990 IJCNN International Joint Conference on

Date of Conference:

17-21 June 1990

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.