Abstract
This paper overviews the application sphere of speaker verification systems and illustrates the use of the Gaussian mixture model and the universal background model (GMM-UBM) in an automatic text-independent speaker verification task. The experimental evaluation of the GMM-UBM system using different speech features is conducted on a 50 speaker set and a result is presented. Equal error rate (EER) using 256 component Gaussian mixture model and feature vector containing 14 mel frequency cepstral coefficients (MFCC) and the voicing probability is 0,76 %. Comparing to standard 14 MFCC vector 23,7 % of EER improvement was acquired.
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Rakhmanenko, I., Meshcheryakov, R. (2016). Speech Features Evaluation for Small Set Automatic Speaker Verification Using GMM-UBM System. In: Ronzhin, A., Potapova, R., Németh, G. (eds) Speech and Computer. SPECOM 2016. Lecture Notes in Computer Science(), vol 9811. Springer, Cham. https://doi.org/10.1007/978-3-319-43958-7_78
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DOI: https://doi.org/10.1007/978-3-319-43958-7_78
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