Communications - Scientific Letters of the University of Zilina 2011, 13(4):20-24 | DOI: 10.26552/com.C.2011.4.20-24

Comparison of Selected Classification Methods in Automatic Speaker Identification

Martin Hric1, Michal Chmulík1, Roman Jarina1
1 Department of Telecommunication and Multimedia, Faculty of Electrical Engineering, University of Zilina, Slovakia

This paper presents performance comparison of three different classifiers applied in Automatic SpeakeR Identification: Gaussian Mixture Model (GMM), k Nearest Neighbor algorithm (kNN) and Support Vector Machines (SVM). Each classifier represents different approach to the classification procedure. Mel Frequency Cepstral Coefficients (MFCC) were used as feature vectors in the experiment. Classification precision for each classifier was evaluated on frame and recording level. Experiments were conducted over dataset MobilDat-SK, which was recorded in mobile telecommunication network. Experiment shows promising results for SVM classifier.

Keywords: kNN, SVM, GMM, MFCC, speaker identification

Published: December 31, 2011  Show citation

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Hric, M., Chmulík, M., & Jarina, R. (2011). Comparison of Selected Classification Methods in Automatic Speaker Identification. Communications - Scientific Letters of the University of Zilina, 13(4), 20-24. doi: 10.26552/com.C.2011.4.20-24
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    References

    1. REYNOLDS, D. A.: Speaker Identification and Verification Using Gaussian Mixture Speaker Models. Speech Communication. Vol 17, No. 1-2, 1995. Go to original source...
    2. BIMBOT, F. et al.: A Tutorial on Text-Independent Speaker Verification, EURASIP J. on Applied Signal Processing. Vol. 4, pp. 430-451, 2004. Go to original source...
    3. XINDONG, W., VIPIN, K.: The Top 10 Algorithms in Data Mining, Chapman & Hall/CRC, 2009.
    4. VAPNIK, V.: Statistical Learning Theory, Wiley, New York, 1998.
    5. HSU, C. W., CHANG, C. C., LIN, C. J.: A Practical Guide to Support Vector Classification, http://www.csie.ntu.edu.tw/~cjlin/libsvm/
    6. JUNLI, C., LICHENG, J.: Classification Mechanism of Support Vector Machines, Proc. of 5th Intern. Conference on Signal Processing, 2000. WCCC-ICSP 2000, Vol.3, 2000, pp.1556-1559.
    7. CHANG, C. C., LIN, C. J.: LIBSVM: A library for Support Vector Machines, 2001. Software available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm
    8. BLONDIN, J., SAAD, A.: Metaheuristic Techniques for Support Vector Machines Model Selection. In 10th Intern. Conference on Hybrid Intelligent Systems. 2010. Go to original source...
    9. RUSKO, M., TRNKA, M., DARJAA, S.: MobilDat-SK a Mobile Telephone Extension to the SpeechDat-E SK Telephone Speech Database in Slovak. In Proc. of the 11th International Conference Speech and Computer (SPECOM'2006), St. Petersburg, 2006, pp. 449-454.
    10. JUHAR, J., ONDAS, S., CIZMAR, A., JARINA, R., RUSKO, M., ROZINAJ, G.: Development of Slovak GALAXY / VoiceXML Based Spoken Language Dialogue System to Retrieve Information from the Internet, Proc. of the Interspeech 2006 - ICSLP, Pittsburg (USA), 2006, pp. 485-488. Go to original source...
    11. TADJ, C., DUMOUCHEL, P., OUELLET, P.: GMM Based Speaker Identification Uusing Training-Time-Dependent Number of Mixtures, Acoustics, Speech and Signal Processing, 1998. Proc. of the 1998 IEEE Intern. Conference on, Vol. 2, 12-15 May 1998, pp. 761-764.
    12. PARALIC, M., JARINA, R.: Variable Component Approach in GMM - Based Speaker Modelling, 3rd Intern. Acoustical Conference - EAA Symposium, Acoustics High Tatras 2006, Strbské Pleso, Slovakia, 2006, pp. 164-167.
    13. NABNEY, I. T.: Netlab: Algorithms for Pattern Recognition. Advances in Pattern Recognition. Springer, Berlin, 2001.

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