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Performance Comparison of Automatic Speaker Recognition using Vector Quantization by LBG KFCG and KMCG
Dr. H B Kekre, Vaishali Kulkarni
Pages - 571 - 579     |    Revised - 31-01-2011     |    Published - 08-02-2011
Volume - 4   Issue - 6    |    Publication Date - January / February  Table of Contents
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KEYWORDS
Speaker Identification, Vector Quantization, Code vectors, KFCG, KMCG, LBG
ABSTRACT
In this paper, three approaches for automatic Speaker Recognition based on Vector quantization are proposed and their performances are compared. Vector Quantization (VQ) is used for feature extraction in both the training and testing phases. Three methods for codebook generation have been used. In the 1st method, codebooks are generated from the speech samples by using the Linde-Buzo-Gray (LBG) algorithm. In the 2nd method, the codebooks are generated using the Kekre’s Fast Codebook Generation (KFCG) algorithm and in the 3rd method, the codebooks are generated using the Kekre’s Median Codebook Generation (KMCG) algorithm. For speaker identification, the codebook of the test sample is similarly generated and compared with the codebooks of the reference samples stored in the database. The results obtained for the three methods have been compared. The results show that KFCG gives better results than LBG, while KMCG gives the best results.
CITED BY (7)  
1 Kulkarni, V. (2013). Speaker identification using orthogonal transforms and vector quantization.
2 Kekre, H. B., & Kulkarni, V. (2013, January). Closed set and open set Speaker Identification using amplitude distribution of different Transforms. In Advances in Technology and Engineering (ICATE), 2013 International Conference on (pp. 1-8). IEEE.
3 Kekre, H. B., & Kulkarni, V. (2011). Performance Comparison of Speaker Identification using circular DFT and WHT Sectors. International Journal of Computer Science and Information Security, 9(3), 139.
4 Kekre, H. B., & Kulkarni, V. (2011, February). Automatic speaker recognition using circular DFT sectors. In Proceedings of the International Conference & Workshop on Emerging Trends in Technology (pp. 1280-1285). ACM.
5 Kekre, D. H., Kulkarni, V., Venkatraman, S., Priya, A., & Narashiman, S. (2011). Speaker Identification using Row Mean of DCT and Walsh Hadamard Transform. International Journal on Computer Science and Engineering, 3(1).
6 H. B. Kekre and V. Kulkarni. “Automatic Speaker Recognition using Circular Sectorization of the DFT Complex Plane”, in Proceedings of International Conference and workshop on Emerging Trends in Technology (ICWET), (5), 2011, pp. 35-41.
7 Dr. H. B. Kekre and V. Kulkarni, “Speaker Identification using Row Mean of DCT and Walsh Hadamard Transform”, International Journal on Computer Science and Engineering (IJCSE), 3(3), pp. 1295- 1301, Mar. 2011.
1 Google Scholar
2 Academic Journals Database
3 CiteSeerX
4 refSeek
5 iSEEK
6 Libsearch
7 Bielefeld Academic Search Engine (BASE)
8 Scribd
9 SlideShare
10 PdfSR
1 Lawrence Rabiner, Biing-Hwang Juang and B.Yegnanarayana, “Fundamental of Speech Recognition”, Prentice-Hall, Englewood Cliffs, 2009.
2 S Furui, “50 years of progress in speech and speaker recognition research”, ECTI Transactions on Computer and Information Technology, Vol. 1, No.2, November 2005.
3 D. A. Reynolds, “An overview of automatic speaker recognition technology”, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP’02), 2002, pp. IV-4072–IV-4075.
4 Joseph P. Campbell, Jr., Senior Member, IEEE, “Speaker Recognition: A Tutorial”, Proceedings of the IEEE, vol. 85, no. 9, pp. 1437-1462, September 1997.
5 F. Bimbot, J.-F. Bonastre, C. Fredouille, G. Gravier, I. Magrin-Chagnolleau, S. Meignier, T. Merlin, J. Ortega-García, D.Petrovska-Delacrétaz, and D. A. Reynolds, “A tutorial on text-independent speaker verification,” EURASIP J. Appl. Signal Process., vol. 2004, no. 1, pp. 430–451, 2004.
6 D. A. Reynolds, “Experimental evaluation of features for robust speaker identification,” IEEE Trans.Speech Audio Process., vol. 2, no. 4, pp. 639–643, Oct. 1994.
7 Tomi Kinnunen, Evgeny Karpov, and Pasi Fr¨anti, “Realtime Speaker Identification”, ICSLP2004.
8 Marco Grimaldi and Fred Cummins, “Speaker Identification using Instantaneous Frequencies”, IEEE Transactions on Audio, Speech, and Language Processing, vol., 16, no. 6, August 2008.
9 Zhong-Xuan, Yuan & Bo-Ling, Xu & Chong-Zhi, Yu. (1999). “Binary Quantization of Feature Vectors for Robust Text-Independent Speaker Identification” in IEEE Transactions on Speech and Audio Processing, Vol. 7, No. 1, January 1999. IEEE, New York, NY, U.S.A.
10 R. M. Gray.: ‘Vector quantization’, IEEE ASSP Marg., pp. 4-29, Apr. 1984.
11 Y. Linde, A. Buzo, and R. M. Gray.: ‘An algorithm for vector quantizer design,” IEEE Trans.Commun.’, vol. COM-28, no. 1, pp. 84-95, 1980.
12 A. Gersho, R.M. Gray.: ‘Vector Quantization and Signal Compression’, Kluwer Academic Publishers,Boston, MA, 1991.
13 F. K. Soong, et. al., “A vector quantization approach to speaker recognition”, At & T Technical Journal, 66, pp. 14-26, 1987.
14 A. E. Rosenberg and F. K. Soong, “Evaluation of a vector quantization talker recognition system in text independent and text dependent models”, Computer Speech and Language 22, pp. 143-157,1987.
15 Jeng-Shyang Pan, Zhe-Ming Lu, and Sheng-He Sun.: ‘An Efficient Encoding Algorithm for Vector Quantization Based on Subvector Technique’, IEEE Transactions on image processing, vol 12 No. 3 March 2003.
16 F. Soong, E. Rosenberg, B. Juang, and L. Rabiner, "A Vector Quantization Approach to Speaker Recognition", AT&T Technical Journal, vol. 66, March/April 1987, pp. 1426.
17 Md. Rashidul Hasan, Mustafa Jamil, Md. Golam Rabbani Md. Saifur Rahman , “Speaker Identification using Mel Frequency Cepstral Coefficients”, 3rd International Conference on Electrical & Computer Engineering ICECE held at Dhaka, Bangladesh , 28-30 December 2004.
18 Poonam Bansal, Amrita Dev, Shail Bala Jain, “Automatic Speaker Identification using Vector Quantization”, Asian Journal of Information Technology 6 (9): 938-942, 2007.
19 Jyoti Singhai, “Automatic Speaker Recognition :An Approach using DWT based Feature Extraction and Vector Quantization”, IETE Technical Review, vol. 24, No 5, pp 395-402, September-October 2007
20 H. B. Kekre, Tanuja K. Sarode, “Speech Data Compression using Vector Quantization”, WASET International Journal of Computer and Information Science and Engineering (IJCISE), Fall 2008,Volume 2, Number 4, pp.: 251-254, 2008. http://www.waset.org/ijcise.
21 H. B. Kekre, Tanuja K. Sarode, “New Fast Improved Codebook Generation Algorithm for Color Images using Vector Quantization,” International Journal of Engineering and Technology, vol.1, No.1,pp. 67-77, September 2008.
22 H. B. Kekre, Tanuja K. Sarode, “Fast Codebook Generation Algorithm for Color Images using Vector Quantization,” International Journal of Computer Science and Information Technology, Vol. 1, No. 1,pp: 7-12, Jan 2009.
23 H. B. Kekre, Tanuja K. Sarode, “An Efficient Fast Algorithm to Generate Codebook for Vector Quantization,” First International Conference on Emerging Trends in Engineering and Technology,ICETET-2008, held at Raisoni College of Engineering, Nagpur, India, 16-18 July 2008, Avaliable at online IEEE Xplore.
24 H B Kekre, Vaishali Kulkarni, “Speaker Identification by using Vector Quantization”, International Journal of Engineering Science and Technology, May 2010 edition.
25 H B Kekre, Vaishali Kulkarni, “Performance Comparison of Speaker Recognition using Vector Quantization by LBG and KFCG”, International Journal of Computer Applications, vol. 3, July 2010.
26 H B Kekre, Tanuja Sarode, “2-level Vector Quantization Method for Codebook Design using Kekre's Median Codebook Generation Algorithm”,International Journal of Advances in Computational Sciences and Technology Year:2009,Volume:2,Issue:2.
27 H.B. Kekre, Archana Athawale, Tanuja K. Sarode, Kalpana Sagvekar, “Comparative Performance of Information Hiding in Vector Quantized Codebooks using LBG, KPE, KMCG and KFCG”, International Journal of Computer Science and Information Security, 2010 Vol: 8 Issue: 2,pp 89-95.
28 H B Kekre, Archana Athawale, Tanuja Sarode and Kalpana Sagvekar, “Increased Capacity of Information Hiding using Mixed Codebooks of Vector Quantization Algorithms: LBG, KPE and KMCG,International Journal of Advances in Computational Sciences and Technology, Volume 3 Number 2(2010) pp. 245–256.
Dr. Dr. H B Kekre
MPSTME, NMIMS - India
hbkekre@yahoo.com
Associate Professor Vaishali Kulkarni
MPSTME, NMIMS - India