The week's pick
Reseach Article
Gaussian Mixture Model: A Modeling Technique for Speaker Recognition and its Component
| Advanced Computing and Communication Techniques for High Performance Applications |
| Foundation of Computer Science USA |
| ICACCTHPA2014 - Number 2 |
| February 2015 |
| Authors: Nilu Singh, Alka Agrawal, R. A. Khan |
| 318dd12d-447b-4249-8ec3-aa7469f65b85 |
Nilu Singh, Alka Agrawal, R. A. Khan . Gaussian Mixture Model: A Modeling Technique for Speaker Recognition and its Component. Advanced Computing and Communication Techniques for High Performance Applications. ICACCTHPA2014, 2 (February 2015), 28-31.
Abstract
This paper provides an overview of Gaussian Mixture Model (GMM) and its component of speech signal. During the earlier period it has been revealed that Gaussian Mixture Model is very much appropriate for voice modeling in speaker recognition system. For Speaker recognition, Gaussian mixture model is an essential appliance of statistical clustering. The task effortlessly performed by humans is not effortless for machine or computers such as voice recognition or face recognition so for this function speaker recognition technology makes available a solution, using this technology the computers/machines outperforms than humans.
References
- Reynolds, Douglas. "Gaussian Mixture Models. " MIT Lincoln Laboratory, 244 Wood St. , Lexington, MA 02140, USA: 1-5. Print.
- Neiberg, Daniel . "Text Independent Speaker Verification Using Adapted Gaussian Mixture Models. " Centre for Speech Technology (CTT) Department of Speech, Music and Hearing KTH, Stockholm, Sweden: 1-47. Print.
- Yegnanarayana, B. , K. Sharat Reddy, and S. P. Kishore. "SOURCE AND SYSTEM FEATURES FOR SPEAKER RECOGNITION USING AANN MODELS. " Speech and Vision Laboratory Department of Computer Science and Engineering Indian Institute of Technology Madras: 1-4. Print
- Alfredo, Maesa, Fabio Garzia, Michele Scarpiniti, and Roberto Cusani. "Text Independent Automatic Speaker Recognition System Using Mel-Frequency Cepstrum Coefficient and Gaussian Mixture Models. " Journal of Information Security, 2012, : 335-340. Print.
- Tran, Dat , and Michael Wagner. "Fuzzy Gaussian Mixture Models for Speaker Recognition. " Human-Computer Communication Laboratory School of Computing, University of Canberra, ACT 2601, Australia: 1-8. Print.
- Plataniotis, K. N. , and D. Hatzinakos. "Gaussian Mixtures and their Applications to Signal Processing. " Gaussian Mixtures and their Applications to Signal Processing. Toronto: Department of Electrical and Computer Engineering University of Toronto , Ontario, M5S 3G4, Canada, 2000. . Print.
- Kamarauskas, J. . "Speaker Recognition using Gaussian Mixture Models. " ELECTRONICS AND ELECTRICAL ENGINEERING 2008. No. 5(85): 29-32. Print.
- "Robust Features for Automatic Text-Independent Speaker Recognition Usin g Gaussian Mixture Model. " International Journal of Soft Computing and Engineering (IJSCE) Volume-1, November 2011: 330-335. Print.
- Q. D'Almeida, Frederico , Francisco A. O. Nascimento, Pedro A. Berger, and Lúcio M. da Silva. "Automatic Speaker Recognition with Multi-resolution Gaussian Mixture Models (MR-GMMs). " The International Journal of FORENSIC COMPUTER SCIENCE: 9-21. Print.
- Reynolds, Douglas A, and Richard C Rose. "Robust Text Independent Speaker Identification Using gaussian Mixture Speaker Model. " IEEE transactions on speech and audio processing Volume 3: 72-83. Print.
- A. Reynolds, Douglas . "Automatic Speaker Recognition Using Gaussian Mixture Speaker Models. " THE LINCOLN LABORATORY JOURNAL VOlUME 8, NUMBER 2,1995 : 173-192. Print.
- CAMPBELL, JOSEPH P. . "Speaker Recognition: A Tutorial. " PROCEEDINGS OF THE IEEE VOL. 85, : 1437-1462. Print.
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