2015 15th International Conference on Intelligent Systems Design and Applications (ISDA) 2015
DOI: 10.1109/isda.2015.7489195
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A hybrid system based on GMM-SVM for speaker identification

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Cited by 7 publications
(3 citation statements)
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“…With help of Nao robot, it's capable to recognize speaker emotions from the recording. Similarly, R. Chakroun, L. B. Zouari, M. Frikha and A. Ben Hamida [24] presented that Support Vector Machine (SVM) is more supportive with GMM for speaker recognition. GMM is more successful for speaker recognition when speaker voice is text independent.…”
Section: Related Workmentioning
confidence: 99%
“…With help of Nao robot, it's capable to recognize speaker emotions from the recording. Similarly, R. Chakroun, L. B. Zouari, M. Frikha and A. Ben Hamida [24] presented that Support Vector Machine (SVM) is more supportive with GMM for speaker recognition. GMM is more successful for speaker recognition when speaker voice is text independent.…”
Section: Related Workmentioning
confidence: 99%
“…Text independent speaker recognition is introduced in [14]. They combined GMM and SVM approaches to improve speaker identification system.…”
Section: Related Workmentioning
confidence: 99%
“…In text-independent speaker verification, support vector machine (SVM) has been proven to be effective classifier and most popularly used for many years. 4 It has many desirable properties inherently, including the ability to classify patterns with least expected risk principle, to classify sparse data 5 without over-training problem, and to make non-linear decisions via kernel function. Sloin and Burshtein 6 presented a discriminative training algorithm based on SVM to improve the classification of hidden Markov models.…”
Section: Introductionmentioning
confidence: 99%