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Incorporating Belief Function in SVM for Phoneme Recognition
lib:20fbc04c4fbe5947 (v1.0.0)
Authors: Rimah Amami,Dorra Ben Ayed,Nouerddine Ellouze
ArXiv: 1507.06025
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1507.06025v1
The Support Vector Machine (SVM) method has been widely used in numerous classification tasks. The main idea of this algorithm is based on the principle of the margin maximization to find an hyperplane which separates the data into two different classes.In this paper, SVM is applied to phoneme recognition task. However, in many real-world problems, each phoneme in the data set for recognition problems may differ in the degree of significance due to noise, inaccuracies, or abnormal characteristics; All those problems can lead to the inaccuracies in the prediction phase. Unfortunately, the standard formulation of SVM does not take into account all those problems and, in particular, the variation in the speech input. This paper presents a new formulation of SVM (B-SVM) that attributes to each phoneme a confidence degree computed based on its geometric position in the space. Then, this degree is used in order to strengthen the class membership of the tested phoneme. Hence, we introduce a reformulation of the standard SVM that incorporates the degree of belief. Experimental performance on TIMIT database shows the effectiveness of the proposed method B-SVM on a phoneme recognition problem.
ArXiv: 1507.06025
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1507.06025v1
The Support Vector Machine (SVM) method has been widely used in numerous classification tasks. The main idea of this algorithm is based on the principle of the margin maximization to find an hyperplane which separates the data into two different classes.In this paper, SVM is applied to phoneme recognition task. However, in many real-world problems, each phoneme in the data set for recognition problems may differ in the degree of significance due to noise, inaccuracies, or abnormal characteristics; All those problems can lead to the inaccuracies in the prediction phase. Unfortunately, the standard formulation of SVM does not take into account all those problems and, in particular, the variation in the speech input. This paper presents a new formulation of SVM (B-SVM) that attributes to each phoneme a confidence degree computed based on its geometric position in the space. Then, this degree is used in order to strengthen the class membership of the tested phoneme. Hence, we introduce a reformulation of the standard SVM that incorporates the degree of belief. Experimental performance on TIMIT database shows the effectiveness of the proposed method B-SVM on a phoneme recognition problem.
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