A confidence measure based — Score fusion technique to integrate MFCC and Pitch for speaker verification

2011 ◽  
Author(s):  
Shanthini Pandiaraj ◽  
H Nisha Rachel Keziah ◽  
D Synthiya Vinothini ◽  
Lineeta Gloria ◽  
K. R. Shankar Kumar
Keyword(s):  
Speaker Verification ◽  
Confidence Measure ◽  
Fusion Technique ◽  
Score Fusion

Confidence measure based unsupervised target model adaptation for speaker verification

2007 ◽  
Author(s):  
A. Preti ◽  
Jean-François Bonastre ◽  
Driss Matrouf ◽  
F. Capman ◽  
B. Ravera
Keyword(s):  
Speaker Verification ◽  
Model Adaptation ◽  
Confidence Measure ◽  
Target Model

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

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.

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