Subword-based speaker verification with multiple-classifier score fusion weight and threshold adaptation

2004 ◽  
Vol 115 (5) ◽  
pp. 1872
Author(s):  
Manish Sharma
Keyword(s):  
Speaker Verification ◽  
Multiple Classifier ◽  
Score Fusion

Speaker verification with multiple classifier fusion using Bayes based confidence measure

2007 ◽  
Author(s):  
Fernando Huenupán ◽  
Nestor Becerra Yoma ◽  
Carlos Molina ◽  
Claudio Garreton
Keyword(s):  
Speaker Verification ◽  
Classifier Fusion ◽  
Confidence Measure ◽  
Multiple Classifier

scholarly journals Feature and Score Fusion Based Multiple Classifier Selection for Iris Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Md. Rabiul Islam
Keyword(s):  
Likelihood Ratio ◽  
Iris Recognition ◽  
Feature Fusion ◽  
Ratio Score ◽  
Multimodal System ◽  
Classifier Selection ◽  
Multiple Classifier ◽  
Score Fusion ◽  
Fusion Approach ◽  
Voting Method

The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existingNhamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

Confidence based multiple classifier fusion in speaker verification

2008 ◽  
Vol 29 (7) ◽  
pp. 957-966 ◽  
Author(s):  
Fernando Huenupán ◽  
Nestor Becerra Yoma ◽  
Carlos Molina ◽  
Claudio Garretón
Keyword(s):  
Speaker Verification ◽  
Classifier Fusion ◽  
Multiple Classifier

Acoustic Features Selection of Speaker Verification Based on Average KL Distance

2013 ◽  
Vol 373-375 ◽  
pp. 629-633
Author(s):  
Yu Luan ◽  
Hong Zuo Li ◽  
Ya Fei Wang
Keyword(s):  
Information Content ◽  
Speaker Verification ◽  
Features Selection ◽  
Acoustic Features ◽  
Selection Algorithm ◽  
Score Fusion ◽  
Optimal Feature Selection ◽  
Matching Score ◽  
Optimal Feature ◽  
Selection Of

This paper proposes a new Average Kullback-Leibler distance to make an optimal feature selection algorithm for the matching score fusion of speaker verification. The advantage of this novel distance is to overcome the shortcoming of the asymmetry of conventional Kullback-Leibler distance, which can ensure the accuracy and robustness of the computation of the information content between matching scores of two acoustic features. From the experimental results by a variety of fusion schemes, it is found that the matching score fusion between MFCC and residual phase gains most information content. It indicates this scheme can yield an excellent performance.

Speaker Verification with Model-based and Score-based Unsupervised Adaptation Meth

2009 ◽  
Vol 35 (3) ◽  
pp. 267-271
Author(s):  
Er-Yu WANG ◽  
Wu GUO ◽  
Yi-Jie LI ◽  
Li-Rong DAI ◽  
Ren-Hua WANG
Keyword(s):  
Speaker Verification ◽  
Model Based
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