Speaker recognition via sparse representations using orthogonal matching pursuit

V, Boominathan and Kodukula, Sri Rama Murty (2012) Speaker recognition via sparse representations using orthogonal matching pursuit. In: International Conference on Acoustics, Speech, and Signal Processing, 25-30, March 2012, Kyoto; Japan.

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The objective of this paper is to demonstrate the effectiveness of sparse representation techniques for speaker recognition. In this approach, each feature vector from unknown utterance is expressed as linear weighted sum of a dictionary of feature vectors belonging to many speakers. The weights associated with feature vectors in the dictionary are evaluated using orthogonal matching pursuit algorithm, which is a greedy approximation to l0 optimization. The weights thus obtained exhibit high level of sparsity, and only a few of them will have nonzero values. The feature vectors which belong to the correct speaker carry significant weights. The proposed method gives an equal error rate (EER) of 10.84% on NIST-2003 database, whereas the existing GMM-UBM system gives an EER of 9.67%. By combining evidence from both the systems an EER of 8.15% is achieved, indicating that both the systems carry complimentary information.

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IITH Creators:
IITH CreatorsORCiD
Kodukula, Sri Rama Murtyhttps://orcid.org/0000-0002-6355-5287
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: l0 optimization and Gaussian mixture modeling; orthogonal matching pursuit; Sparse representation; Equal error rate; Feature vectors; Gaussian mixture modeling; Greedy approximation; Nonzero values; Orthogonal matching pursuit; Sparse representation; Speaker recognition; Weighted Sum
Subjects: Physics > Electricity and electronics
Divisions: Department of Electrical Engineering
Depositing User: Users 3 not found.
Date Deposited: 15 Oct 2014 09:28
Last Modified: 05 Dec 2017 04:07
URI: http://raiith.iith.ac.in/id/eprint/446
Publisher URL: https://doi.org/10.1109/ICASSP.2012.6288890
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