By Topic

Feature selection through orthogonal expansion in isolated word recognition

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)
Lleida, E. ; Dpto. Teoria de la Senal y Commun., Univ. Politecnica de Cataluna, Barcelona, Spain ; Nadeu, C. ; Marino, J.B.

The use of an orthogonal expansion for feature selection and data compression in isolated word recognition is presented. Assuming that the spectral evolution, given by a LPC analysis, is a noisy measure in the sense that it is a linear combination of a set of real features, the objective of the orthogonal expansion is to find these real features. The new feature vectors are used to perform the recognition process. The recognition results as well as the meaning of the orthogonal expansion are discussed

Published in:

Electrotechnical Conference, 1989. Proceedings. 'Integrating Research, Industry and Education in Energy and Communication Engineering', MELECON '89., Mediterranean

Date of Conference:

11-13 Apr 1989