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Title:
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Maximum likelihood Linear Programming Data Fusion for Speaker Recognition
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Author:
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Monte-Moreno, Enric; Chetouani, Mohamed; Faundez-Zanuy, Marcos; Solé-Casals, Jordi
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Other authors:
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Universitat de Vic. Escola Politècnica Superior; Universitat de Vic. Grup de Recerca en Tecnologies Digitals |
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Notes:
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Biometric system performance can be improved by means of data fusion. Several kinds of
information can be fused in order to obtain a more accurate classification (identification or
verification) of an input sample. In this paper we present a method for computing the
weights in a weighted sum fusion for score combinations, by means of a likelihood model.
The maximum likelihood estimation is set as a linear programming problem. The scores are
derived from a GMM classifier working on a different feature extractor. Our experimental
results assesed the robustness of the system in front a changes on time (different sessions)
and robustness in front a change of microphone. The improvements obtained were
significantly better (error bars of two standard deviations) than a uniform weighted sum or a
uniform weighted product or the best single classifier. The proposed method scales
computationaly with the number of scores to be fussioned as the simplex method for linear
programming. |
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Subject(s):
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-Veu, Processament de |
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Rights:
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(c) 2009 Elsevier. Published article is available at: http://dx.doi.org/10.1016/j.specom.2008.05.009
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Document type:
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Article
info:eu-repo/acceptedVersion |
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Published by:
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Elsevier
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Share:
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