کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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565375 | 875749 | 2009 | 11 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Maximum likelihood linear programming data fusion for speaker recognition Maximum likelihood linear programming data fusion for speaker recognition](/preview/png/565375.png)
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 different feature extraction techniques. Our experimental results assessed the robustness of the system in front changes on time (different sessions) and robustness in front of changes 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 computationally with the number of scores to be fusioned as the simplex method for linear programming.
Journal: Speech Communication - Volume 51, Issue 9, September 2009, Pages 820–830