Welcome to the InfoSci Platform
Reference Hub2
Speaker Discrimination on Broadcast News and Telephonic Calls Using a Fusion of Neural and Statistical Classifiers

Speaker Discrimination on Broadcast News and Telephonic Calls Using a Fusion of Neural and Statistical Classifiers

Siham Ouamour, Halim Sayoud
Copyright: © 2009 |Volume: 1 |Issue: 4 |Pages: 17
ISSN: 1937-9412|EISSN: 1937-9404|ISSN: 1937-9412|EISBN13: 9781616921071|EISSN: 1937-9404|DOI: 10.4018/jmcmc.2009072804
Cite Article Cite Article

MLA

Ouamour, Siham, and Halim Sayoud. "Speaker Discrimination on Broadcast News and Telephonic Calls Using a Fusion of Neural and Statistical Classifiers." IJMCMC vol.1, no.4 2009: pp.47-63. http://doi.org/10.4018/jmcmc.2009072804

APA

Ouamour, S. & Sayoud, H. (2009). Speaker Discrimination on Broadcast News and Telephonic Calls Using a Fusion of Neural and Statistical Classifiers. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 1(4), 47-63. http://doi.org/10.4018/jmcmc.2009072804

Chicago

Ouamour, Siham, and Halim Sayoud. "Speaker Discrimination on Broadcast News and Telephonic Calls Using a Fusion of Neural and Statistical Classifiers," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 1, no.4: 47-63. http://doi.org/10.4018/jmcmc.2009072804

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This article describes a new Speaker Discrimination System (SDS), which is a part of an overall project called Audio Documents Indexing based on a Speaker Discrimination System (ADISDS). Speaker discrimination consists in checking whether two speech segments come from the same speaker or not. This research domain presents an important field in biometry, since the voice remains an important feature used at distance (via telephone). However, although some discriminative classifiers do exist nowadays, their performances are not enough sufficient for short speech segments. This issue led us to propose an efficient fusion between such classifiers in order to enhance the discriminative performance. This fusion is obtained, by using three different techniques: a serial fusion, parallel fusion and serial-parallel fusion. Also, two classifiers have been chosen for the evaluation: a mono-gaussian statistical classifier and a Multi Layer Perceptron (MLP). Several experiments of speaker discrimination are conducted on different databases: Hub4 Broadcast-News and telephonic calls. Results show that the fusion has efficiently improved the scores obtained by each approach alone. So, for instance, we got an Equal Error Rate (EER) of about 7% on a subset of Hub4 Broadcast-News database, with short segments of 4 seconds, and an EER of about 4% on telephonic speech, with medium segments of 10 seconds.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.