Verification effectiveness in open-set speaker identification

2006 ◽  
Vol 153 (5) ◽  
pp. 618 ◽  
Author(s):  
A.M. Ariyaeeinia ◽  
J. Fortuna ◽  
P. Sivakumaran ◽  
A. Malegaonkar
Keyword(s):  
Speaker Identification ◽  
Open Set

Open-set speaker identification using adapted Gaussian mixture models

2005 ◽  
Author(s):  
J. Fortuna ◽  
P. Sivakumaran ◽  
A. Ariyaeeinia ◽  
A. Malegaonkar
Keyword(s):  
Mixture Models ◽  
Speaker Identification ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Open Set

Open-set speaker identification with classifier systems

2006 ◽  
Author(s):  
Jae C. Oh ◽  
Misty Blowers
Keyword(s):  
Speaker Identification ◽  
Classifier Systems ◽  
Open Set

scholarly journals Open-set speaker identification under mismatch conditions

2009 ◽  
Author(s):  
S. G. Pillay ◽  
A. Ariyaeeinia ◽  
P. Sivakumaran ◽  
M. Pawlewski
Keyword(s):  
Speaker Identification ◽  
Open Set

scholarly journals Using Neural Network with Speaker Applications

2010 ◽  
Vol 7 (2) ◽  
pp. 1076-1081
Author(s):  
Baghdad Science Journal
Keyword(s):  
Neural Network ◽  
Automatic Speech Recognition ◽  
Multilayer Perceptron ◽  
Speaker Identification ◽  
Test Results ◽  
Identification Performance ◽  
Open Set ◽  
Present Test ◽  
Hidden Layer ◽  
Timit Database

In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty. We also present a method for selecting the speakers used for MLP training which further improves identification performance.

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