List of Journals    /    Call For Papers    /    Subscriptions    /    Login
 
 
 
 
 SEARCH
By Author By Title
 
 
ABOUT CSC
 About CSC Journals
 CSC Journals Objectives
 List of Journals
 CALL FOR PAPERS
 Call For Papers CFP
 Special Issue CFP
AUTHOR GUIDELINES
 Submission Guidelines
 Peer Review Process
 Helpful Hints For Getting Published
 Plagiarism Policies
 Abstracting & Indexing
 Open Access Policy
 Submit Manuscript
 FOR REVIEWERS
 Reviewer Guidelines
 FOR EDITORIAL
 Editor Guidelines
 Join Us As Editor
 Launch Special Issue
 Suggest New Journal
 CSC LIBRARY
 Browse CSC Library
 Open Access Policy
  SERVICES
 Conference Partnership Program (CPP)
 Abstracting & Indexing
 SUBSCRIPTIONS
 Subscriptions
 Discounted Packages
 Archival Subscriptions
 How to Subscribe
 Librarians
 Subscriptions Agents
 Order Form
 DOWNLOADS
 
 
 
 
Performance Comparison Of 2-D DCT On Full/Block Spectrogram And 1-D DCT on Row Mean of Spectrogram for Speaker Identification
Full text
 PDF(134.7KB)
Source 
International Journal of Biometrics and Bioinformatics (IJBB)
Table of Contents
Download Complete Issue    PDF(1.42MB)
Volume:  4    Issue:  3
Pages:  100-135
Publication Date:   July 2010
ISSN (Online): 1985-2347
Pages 
100 - 112
Author(s)  
 
Published Date   
10-08-2010 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Speaker identification, , Speaker Recognition, , Spectrograms, , DCT, , Row Mean 
 
 
This Manuscript is indexed in the following databases/websites:-
1. Scribd
2. Docstoc
3. Directory of Open Access Journals (DOAJ)
4. PDFCAST
5. Google Scholar
6. Academic Index
7. Bielefeld Academic Search Engine (BASE)
8. refSeek
9. Socol@r
10. iSEEK
11. Libsearch
12. slideshare
13. ResearchGATE
14. Academic Journals Database
 
 
The goal of this paper is to present a very simple approach to text dependent speaker identification using a combination of spectrograms and well known Discrete Cosine Transform (DCT). This approach is based on use of DCT to find similarities between spectrograms obtained from speech samples. The set of spectrograms forms the database for our experiments rather than raw speech samples. Performance of this approach is compared for different number of coefficients of DCT when DCT is applied on entire spectrogram, when DCT is applied to spectrogram divided into blocks and when DCT is applied to the Row Mean of a spectrogram. Performance comparison shows that, number of mathematical computations required for DCT on Row Mean of spectrogram method is drastically less as compared to other two methods with almost equal identification rate. 
 
 
 
1 Evgeniy Gabrilovich, Alberto D. Berstin: “Speaker recognition: using a vector quantization approach for robust text-independent speaker identification”, Technical report DSPG-95-9-001’, September 1995.
2 Tridibesh Dutta, “Text dependent speaker identification based on spectrograms”, Proceedings of Image and vision computing, pp. 238-243, New Zealand 2007,
3 J.P.Campbell, “Speaker recognition: a tutorial”, Proc. IEEE, vol. 85, no. 9, pp. 1437-1462, 1997.
4 D. O’Shaughnessy, “Speech communications- Man and Machine”, New York, IEEE Press, 2nd Ed., pp. 199, pp. 437-458, 2000.
5 S. Davis and P. Mermelstein, “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences,” IEEE Transaction Acoustics Speech and Signal Processing, vol. 4, pp. 375-366, 1980.
6 Wang Yutai, Li Bo, Jiang Xiaoqing, Liu Feng, Wang Lihao, “Speaker Recognition Based on Dynamic MFCC Parameters”, International Conference on Image Analysis and Signal Processing, pp. 406-409, 2009
7 Azzam Sleit, Sami Serhan, and Loai Nemir, “A histogram based speaker identification technique”, International Conference on ICADIWT, pp. 384-388, May 2008.
8 B. S. Atal, “Automatic Recognition of speakers from their voices”, Proc. IEEE, vol. 64, pp. 460-475, 1976.
9 Jialong He, Li Liu, and G¨unther Palm, “A discriminative training algorithm for VQ-based speaker Identification”, IEEE Transactions on speech and audio processing, vol. 7, No. 3, pp. 353-356, May 1999.
10 Debadatta Pati, S. R. Mahadeva Prasanna, “Non-Parametric Vector Quantization of Excitation Source Information for Speaker Recognition”, IEEE Region 10 Conference, pp. 1-4, Nov. 2008.
11 Tridibesh Dutta and Gopal K. Basak, “Text dependent speaker identification using similar
12 Andrew B. Watson, “Image compression using the Discrete Cosine Transform”, Mathematica journal, 4(1), pp. 81-88, 1994,.
13 http://www.itee.uq.edu.au/~conrad/vidtimit/
14 http://www2.imm.dtu.dk/~lf/elsdsr/
15 H.B.Kekre, Sudeep D. Thepade, “Improving the Performance of Image Retrieval using Partial Coefficients of Transformed Image”, International Journal of Information Retrieval (IJIR), Serials Publications, Volume 2, Issue 1, pp. 72-79 (ISSN: 0974-6285), 2009.
16 H.B.Kekre, Tanuja Sarode, Sudeep D. Thepade, “DCT Applied to Row Mean and Column Vectors in Fingerprint Identification”, In Proceedings of International Conference on Computer Networks and Security (ICCNS), 27-28 Sept. 2008, VIT, Pune.
17 H.B.Kekre, Sudeep D. Thepade, Archana Athawale, Anant Shah, Prathmesh Verlekar, Suraj Shirke,“Energy Compaction and Image Splitting for Image Retrieval using Kekre Transform over Row and Column Feature Vectors”, International Journal of Computer Science and Network Security (IJCSNS),Volume:10, Number 1, January 2010, (ISSN: 1738-7906) Available at www.IJCSNS.org.
18 H.B.Kekre, Sudeep D. Thepade, Archana Athawale, Anant Shah, Prathmesh Verlekar, Suraj Shirke, “Performance Evaluation of Image Retrieval using Energy Compaction and Image Tiling over DCT Row Mean and DCT Column Mean”, Springer-International Conference on Contours of Computing Technology (Thinkquest-2010), Babasaheb Gawde Institute of Technology, Mumbai, 13-14 March 2010, The paper will be uploaded on online Springerlink.
19 ] H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, Vaishali Suryavanshi, “Improved Texture Feature Based Image Retrieval using Kekre’s Fast Codebook Generation Algorithm”, Springer-International Conference on Contours of Computing Technology (Thinkquest-2010), Babasaheb Gawde Institute of Technology, Mumbai, 13-14 March 2010, The paper will be uploaded on online Springerlink.
20 H. B. Kekre, Tanuja K. Sarode, Sudeep D. Thepade, “Image Retrieval by Kekre’s Transform Applied on Each Row of Walsh Transformed VQ Codebook”, (Invited), ACM-International Conference and Workshop on Emerging Trends in Technology (ICWET 2010),Thakur College of Engg. And Tech., Mumbai, 26-27 Feb 2010, The paper is invited at ICWET 2010. Also will be uploaded on online ACM Portal.
21 H. B. Kekre, Tanuja Sarode, Sudeep D. Thepade, “Color-Texture Feature based Image Retrieval using DCT applied on Kekre’s Median Codebook”, International Journal on Imaging (IJI), Volume 2, Number A09, Autumn 2009,pp. 55-65. Available online at www.ceser.res.in/iji.html (ISSN: 0974-0627).
22 H. B. Kekre, Ms. Tanuja K. Sarode, Sudeep D. Thepade, "Image Retrieval using Color-Texture Features from DCT on VQ Codevectors obtained by Kekre’s Fast Codebook Generation", ICGST-International Journal on Graphics, Vision and Image Processing (GVIP), Volume 9, Issue 5, pp.: 1-8, September 2009. Available online at http://www.icgst.com/gvip/Volume9/Issue5/P1150921752.html.
23 H. B. Kekre, Sudeep Thepade, Akshay Maloo, “Image Retrieval using Fractional Coefficients of Transformed Image using DCT and Walsh Transform”, International Journal of Engineering Science and Technology, Vol.. 2, No. 4, 2010, 362-371
24 H. B. Kekre, Sudeep Thepade, Akshay Maloo,”Performance Comparison of Image Retrieval Using Fractional Coefficients of Transformed Image Using DCT, Walsh, Haar and Kekre’s Transform”, CSC-International Journal of Image processing (IJIP), Vol.. 4, No.2, pp.:142-155, May 2010.
25 H. B. Kekre, Tanuja Sarode “Two Level Vector Quantization Method for Codebook Generation using Kekre’s Proportionate Error Algorithm” , CSC-International Journal of Image Processing, Vol.4, Issue 1, pp.1-10, January-February 2010
26 H. B. Kekre, Sudeep Thepade, Akshay Maloo, “Eigenvectors of Covariance Matrix using Row Mean and Column Mean Sequences for Face Recognition”, CSC-International Journal of Biometrics and Bioinformatics (IJBB), Volume (4): Issue (2), pp. 42-50, May 2010.
 
 
 
1 Dr. H. B. Kekre ,Dr. T. K. Sarode ,S. J. Natu and Pr. J. Natu, “Performance Comparison of Speaker Identification Using DCT, Walsh, Haar on Full and Row Mean of Spectrogram ”, International Journal of Computer Applications, 5(6), pp. 30–37, August 2010.
2 H. B. Kekre, T. K. Sarode and M. S. Ugale, “An Efficient Image Classifier Using Discrete Cosine Transform”, in Proceedings of the International Conference & Workshop on Emerging Trends in Technology, New York, NY, USA 2011.
3 H.B. Kekre , T. K. Sarode and M. S. Ugale, “Performance Comparison of Image Classifier using Discrete Cosine Transform and Walsh Transform” in IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET) (4), 2011, pp. 14-20.
4 Dr. H. B. Kekre , Dr. T. K. Sarode , P. Bhatia , S. N. Nayak and D. J. Nagpal, “Iris Recognition using Partial Coefficients by applying Discrete Cosine Transform, Haar Wavelet and DCT Wavelet Transform” International Journal of Computer Applications 32(6), pp. 39-43, October 2011.
 
 
 
 
 
H. B. Kekre : Colleagues
Tanuja Kiran Sarode : Colleagues
Shachi J. Natu : Colleagues
Prachi J. Natu : Colleagues  
 
 
 
  Untitled Document
 
Copyrights (c) 2012 Computer Science Journals. All rights reserved.
Best viewed at 1152 x 864 resolution. Microsoft Internet Explorer.
 
  
 
Copyrights & Usage: Articles published by CSC Journals are Open Access. Permission to copy and distribute any other content, images, animation and other parts of this website is prohibited. CSC Journals has the rights to take action against individual/group if they are found victim of copying these parts of the website.