By Topic

Recurrent neural network speech predictor based on dynamical systems approach

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 $31
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)
Varoglu, E. ; Dept. of Electr. & Electron. Eng., Eastern Mediterranean Univ., Magosa, Turkey ; Hacioglu, K.

A nonlinear predictive model of speech, based on the method of time delay reconstruction, is presented and approximated using a fully connected recurrent neural network (RNN) followed by a linear combiner. This novel combination of the well established approaches for speech analysis and synthesis is compared with traditional techniques within a unified framework to illustrate the advantages of using an RNN. Extensive simulations are carried out to justify the expectations. Specifically, the network's robustness to the selection of reconstruction parameters, the embedding time delay and dimension, is intuitively discussed and experimentally verified. In all cases, the proposed network was found to be a good solution for both prediction and synthesis

Published in:

Vision, Image and Signal Processing, IEE Proceedings -  (Volume:147 ,  Issue: 2 )