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SFSR
Speaker Verification with the Constraint of Limited Data
Thyamagondlu Renukamurthy Jayanthi Kumari and Haradagere Siddaramaiah Jayanna
Page: 807~823, Vol. 14, No.4, 2018
10.3745/JIPS.01.0030
Keywords: Gaussian Mixture Model (GMM), GMM-UBM, Multiple Frame Rate (MFR), Multiple Frame Size (MFS), MFSR, SFSR
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Speaker Verification with the Constraint of Limited Data
Thyamagondlu Renukamurthy Jayanthi Kumari and Haradagere Siddaramaiah Jayanna
Page: 807~823, Vol. 14, No.4, 2018

Keywords: Gaussian Mixture Model (GMM), GMM-UBM, Multiple Frame Rate (MFR), Multiple Frame Size (MFS), MFSR, SFSR
Show / Hide Abstract
Speaker verification system performance depends on the utterance of each speaker. To verify the speaker,
important information has to be captured from the utterance. Nowadays under the constraints of limited
data, speaker verification has become a challenging task. The testing and training data are in terms of few
seconds in limited data. The feature vectors extracted from single frame size and rate (SFSR) analysis is not
sufficient for training and testing speakers in speaker verification. This leads to poor speaker modeling during
training and may not provide good decision during testing. The problem is to be resolved by increasing
feature vectors of training and testing data to the same duration. For that we are using multiple frame size
(MFS), multiple frame rate (MFR), and multiple frame size and rate (MFSR) analysis techniques for speaker
verification under limited data condition. These analysis techniques relatively extract more feature vector
during training and testing and develop improved modeling and testing for limited data. To demonstrate this
we have used mel-frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC)
as feature. Gaussian mixture model (GMM) and GMM-universal background model (GMM-UBM) are used
for modeling the speaker. The database used is NIST-2003. The experimental results indicate that, improved
performance of MFS, MFR, and MFSR analysis radically better compared with SFSR analysis. The
experimental results show that LPCC based MFSR analysis perform better compared to other analysis
techniques and feature extraction techniques.