By Topic

Vector Quantization Mappings for Speaker Verification

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

2 Author(s)
Brew, A. ; Univ. Coll. Dublin, Dublin, Ireland ; Cunningham, P.

In speaker verification several techniques have emerged to map variable length utterances into a fixed dimensional space for classification. One popular approach uses Maximum A-Posteriori (MAP) adaptation of a Gaussian Mixture Model (GMM) to create a super-vector. This paper investigates using Vector Quantisation (VQ) as the global model to provide a similar mapping. This less computationally complex mapping gives comparable results to its GMM counterpart while also providing the ability for an efficient iterative update enabling media files to be scanned with a fixed length window.

Published in:

Pattern Recognition (ICPR), 2010 20th International Conference on

Date of Conference:

23-26 Aug. 2010