A novel discriminant locality preserving projections for MDM-based speaker classification

Yang, Y; Bao, C

A novel discriminant locality preserving projections for MDM-based speaker classification

Yang, Y

Bao, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2012 3rd Global Congress on Intelligent Systems, GCIS 2012, 2012, pp. 127 - 130
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Bao, C	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings - 2012 3rd Global Congress on Intelligent Systems, GCIS 2012, 2012, pp. 127 - 130	en_US
dc.identifier.isbn	9780769548609	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119905
dc.description.abstract	Speaker classification is an important component for audio indexing technology for many applications such as multimedia conferencing. The primary input device of NIST speaker classification evaluation is Multiple Distant Microphones (MDM). MDM is composed of multiple microphones and has the merit of low price and easy-to-use. The spatial time-delay vector of MDM can be extracted as the speaker's discriminant feature. However the feature dimension will be expanded quickly with the increasing number of sensors. Locality Preserving Projections (LPP) and Discriminant locality preserving projection (DLPP) are the principal manifold dimension-reduction algorithms being proposed recently. In this paper, we proposed a novel method to overcome the drawbacks of traditional manifold algorithms such as the lack of class information or spatial identification information. Some basic concepts of spatial time-delay feature and merging feature for MDM speaker classification are first introduced. A review of known DLPP algorithm followed by Fisher criterion is given. Then the Multi-component Discriminant Locality Preserving Projections (MDLPP) method for speaker classification with MDM is described. Comparative experiment results on real meeting data showed the effectiveness of the proposed method. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2012 3rd Global Congress on Intelligent Systems, GCIS 2012	en_US
dc.relation.isbasedon	10.1109/GCIS.2012.55	en_US
dc.title	A novel discriminant locality preserving projections for MDM-based speaker classification	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Speaker classification is an important component for audio indexing technology for many applications such as multimedia conferencing. The primary input device of NIST speaker classification evaluation is Multiple Distant Microphones (MDM). MDM is composed of multiple microphones and has the merit of low price and easy-to-use. The spatial time-delay vector of MDM can be extracted as the speaker's discriminant feature. However the feature dimension will be expanded quickly with the increasing number of sensors. Locality Preserving Projections (LPP) and Discriminant locality preserving projection (DLPP) are the principal manifold dimension-reduction algorithms being proposed recently. In this paper, we proposed a novel method to overcome the drawbacks of traditional manifold algorithms such as the lack of class information or spatial identification information. Some basic concepts of spatial time-delay feature and merging feature for MDM speaker classification are first introduced. A review of known DLPP algorithm followed by Fisher criterion is given. Then the Multi-component Discriminant Locality Preserving Projections (MDLPP) method for speaker classification with MDM is described. Comparative experiment results on real meeting data showed the effectiveness of the proposed method. © 2012 IEEE.

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http://hdl.handle.net/10453/119905