Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Discriminating coding applied to the Automatic Speaker Identification

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

4 Author(s)

In this paper we focus on the speech signal encoding applied to Automatic Speaker Identification system. We present the extension to the nonlinear field of the linear predictive coding (LPC) method usually used in ASI system. This extension is based on a neural network multilayer perceptron (MLP) in the context of prediction, and it is called Neural Predictive Coding (NPC). We present an experimental study from the Numenta Speakers database. A comparative study with the other traditional coding methods LPC and MFCC are explored. Advantages and disadvantages of each method are discussed, the effects introduced by the speech coding and the speakers number were taken into account. The Results indicate that an improvement in recognition rate and the ASI system complexity by minimizing the necessary feature number by using the NPC feature extraction.

Published in:

Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 2012 6th International Conference on

Date of Conference:

21-24 March 2012