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Text Dependent Speaker Identification using Row Mean Vector of Variable Sized Spectrogram

Published on None 2011 by Dr. H. B. Kekre, Archana Athawale, Mrunali Desai
journal_cover_thumbnail
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET - Number 1
None 2011
Authors: Dr. H. B. Kekre, Archana Athawale, Mrunali Desai
106250b0-8637-41a8-ac4a-0440b2bed80d

Dr. H. B. Kekre, Archana Athawale, Mrunali Desai . Text Dependent Speaker Identification using Row Mean Vector of Variable Sized Spectrogram. International Conference and Workshop on Emerging Trends in Technology. ICWET, 1 (None 2011), 43-47.

@article{
author = { Dr. H. B. Kekre, Archana Athawale, Mrunali Desai },
title = { Text Dependent Speaker Identification using Row Mean Vector of Variable Sized Spectrogram },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { None 2011 },
volume = { ICWET },
number = { 1 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 43-47 },
numpages = 5,
url = { /proceedings/icwet/number1/2065-aca171/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A Dr. H. B. Kekre
%A Archana Athawale
%A Mrunali Desai
%T Text Dependent Speaker Identification using Row Mean Vector of Variable Sized Spectrogram
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET
%N 1
%P 43-47
%D 2011
%I International Journal of Computer Applications
Abstract

In this paper a simple approach to text dependent speaker identification using spectrograms and row mean is presented. This, mainly, revolves around trapping the complex patterns of variation in frequency and amplitude with time while an individual utters a given word through histogram equalized spectrogram. These histogram equalized spectrograms are used as a database to successfully identify the unknown individual from his/her voice. The features used for identifying, rely on optimal spectrogram segmentation and the Euclidean distance of the distributional features of the spectrograms of the unknown voice with that of a given known speaker in the database. Performance of this novel approach on a sample collected as two separate databases from 12 speakers and 28 speakers show that this methodology can be effectively used to produce a desirable success rate.

References
  1. Abdul Manan Ahmad, Loh Mun Yee “Vector Quantization Decision Function for Gaussian Mixture Model Based Speaker Identification”, 2008 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS2008) Swissôtel Le Concorde,Bangkok,Thailand
  2. Ali Zulfiqar, A. Muhammad, A. Enriquez, A.M., “A Speaker Identification System using MFCC Features with VQ Technique”, 2009 Third International Symposium on Intelligent Information Technology Application, Vol 3, pp 115-118.
  3. Bojan Imperl, “Speaker recognition techniques”, Laboratory for Digital Signal Processing, Faculty of Electrical Engineering and Comp. Sci., Smetanova 17, 2000 Maribor, Slovenia.
  4. Dr. H. B. Kekre, S D Thepade, A Athawale, A Shah, P Verlekar, S Shirke, “Image Retrieval using DCT on Row Mean, Column Mean and Both with Image Fragmentation”, International Conference and Workshop on Emerging Trends in Technology (ICWET 2010) – TCET, Mumbai, India, February 26-27, 2010.
  5. Dr. H. B. Kekre, Dr. Tanuja K. Sarode, Shachi J. Natu, Prachi J. Natu, “Speaker Identification Using 2-D DCT, Walsh And Haar On Full And Block Spectrogram”, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 05, 2010, 1733-1740.
  6. Tridibesh Dutta, “Text dependent speaker identification based on spectrograms”, Proceedings of Image and vision computing, pp. 238-243, New Zealand 2007.
  7. Y. Linde, A. Buzo, R. M. Gray, “An algorithm for Vector Quantizer Design”, IEEE Transaction on Communications, 28: 1980, pp 84-95.
Index Terms

Computer Science
Information Sciences

Keywords

Speaker Identification Speaker Recognition Histogram Spectrograms Row Mean