A Research On Information Fusion Algorithms In Voice Biometrics

ABSTRACT:This thesis aims to improve performance of voice biometrcis system through different investigation of feature level fusion, matching-score level fusion, decision-making level fusion and multiple level fusion algorithms, in order to solve the problems related to public security furtherly. By the discussions of three different level fusion frameworks, the thesis strengthens the accuaccay of the system by aspects of the establishment of feature level fusion, feature selection for matching-score level fusion, and multiple level fusion. The main contributions are shown as follows:1. According to three fusion levels, firstly it summrises current information fusion algorithms on speaker recognition, then makes subcatrgories for the matching-score level fusion. By investigating problems encounted in each fusion level, the following contributions have been proposed:2. For the feature level, a Relation Measurement Fusion framework-based feature level fusion algorithm on speaker verification has been proposed which superior to the existing fusion methods. According to the robustness and availability of the Relation Measurement Fusion framework, the feature level fusion on speaker verification is established. In order to show advantage of feature level fusion, the Maximum Kullback-Leibler distance is firstly introduced to measure information content for feature level and matching-score level fusions. The exprimental results indicate the feature level fusion can hold more discriminative information amount to obtain lower EER and MinDCF than the existing matching-score level fusion and unimodal algorithms. In the best case, compared to the matching score level fusion and unimodal algorithm, EER of the proposed algorithm improves 3.88% and 7.3%.3. For the matching score level, a Spearman rank correlation coefficient-based feature selection algorithm for the matching-score level fusion has been proposed. Fusion techniques by using different features have been employed, but no metric is used to measure correlation for combined features on the matching-score level fusion so far. So an attempt by making use of the Spearman rank correlation coefficient is described as a metric to measure correlation for the matching-score level fusion of speaker recognition. In this context, this metric is able to find out an optimized selection the combination of MFCC and residual phase to achieve good performance. Then, polynomial curve fitting is employed to describe the relationships between the Spearman coefficient and EER or MinDCF, tesifying the availability of the Spearman coefficient. After that, Kullback-Leibler distance is used to verifie that the availability of Spearman coefficient again. Finally, compared with other correlation metrics, the time cost of the Spearman coefficients outperforms others.4. For decision-making level, a multiple level fusion framework has been proposed. Based on this framework, both a strong multiple level fusion and three weak multiple level fusion have been defined. By discussing these four multiple level fusion cases, finally a two-feature muitiple level fusion algorithm which combines matching-score level fusion and decision-making level fusion has been proposed. From the experimental results, this algorithm has shown the theory of the multiple level fusion has the avaibility, and is superior to the current maching-score level fusion and unimodal algorithm, reducing 18.63% of EER compared with unimodal algorithm in the best case.