By Topic

An experimental comparison of different feature extraction and classification methods for telephone speech

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

1 Author(s)
Schurer, T. ; Inst. fur Fernmeldetech., Tech. Univ. Berlin, Germany

Robust speech recognition over telephone lines severely depends on the choice of the feature extraction and classification methods. In order to get the highest possible performance of the speech recognizer a number of commonly used feature extraction methods (MFCC, LPC, PLP, RASTA-PLP) and classification methods (MLP, LVQ, HMM) were tested on the same telephone speech data. All combinations of feature extraction and classification methods were computed and several parameters of both methods where changed in order to find a non-local maximum of recognition accuracy. The paper does not describe a comparison of classification but of feature extraction methods because it is clear that an HMM would outperform both LVQ and MLP. The big question is if the same feature extraction methods always lead to the best results, no matter which classifier is used!

Published in:

Interactive Voice Technology for Telecommunications Applications, 1994., Second IEEE Workshop on

Date of Conference:

26-27 Sep 1994