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Speaker Discrimination on Broadcast News and Telephonic Calls Based on New Fusion Techniques

Speaker Discrimination on Broadcast News and Telephonic Calls Based on New Fusion Techniques

Halim Sayoud, Siham Ouamour
Copyright: © 2011 |Pages: 18
ISBN13: 9781609605636|ISBN10: 1609605632|EISBN13: 9781609605643
DOI: 10.4018/978-1-60960-563-6.ch017
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MLA

Sayoud, Halim, and Siham Ouamour. "Speaker Discrimination on Broadcast News and Telephonic Calls Based on New Fusion Techniques." Innovations in Mobile Multimedia Communications and Applications: New Technologies, edited by Ismail Khalil and Edgar R. Weippl, IGI Global, 2011, pp. 244-261. https://doi.org/10.4018/978-1-60960-563-6.ch017

APA

Sayoud, H. & Ouamour, S. (2011). Speaker Discrimination on Broadcast News and Telephonic Calls Based on New Fusion Techniques. In I. Khalil & E. Weippl (Eds.), Innovations in Mobile Multimedia Communications and Applications: New Technologies (pp. 244-261). IGI Global. https://doi.org/10.4018/978-1-60960-563-6.ch017

Chicago

Sayoud, Halim, and Siham Ouamour. "Speaker Discrimination on Broadcast News and Telephonic Calls Based on New Fusion Techniques." In Innovations in Mobile Multimedia Communications and Applications: New Technologies, edited by Ismail Khalil and Edgar R. Weippl, 244-261. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-563-6.ch017

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Abstract

This chapter 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, the authors 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.

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