CFP last date
20 January 2025
Reseach Article

Article:Pass-Phrase based Speaker Identification

by Y.K.Viswanadham, T.V.Subrahmanyam, I.Leela Priya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 8
Year of Publication: 2010
Authors: Y.K.Viswanadham, T.V.Subrahmanyam, I.Leela Priya
10.5120/1504-2022

Y.K.Viswanadham, T.V.Subrahmanyam, I.Leela Priya . Article:Pass-Phrase based Speaker Identification. International Journal of Computer Applications. 10, 8 ( November 2010), 6-9. DOI=10.5120/1504-2022

@article{ 10.5120/1504-2022,
author = { Y.K.Viswanadham, T.V.Subrahmanyam, I.Leela Priya },
title = { Article:Pass-Phrase based Speaker Identification },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 10 },
number = { 8 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume10/number8/1504-2022/ },
doi = { 10.5120/1504-2022 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:59:09.730292+05:30
%A Y.K.Viswanadham
%A T.V.Subrahmanyam
%A I.Leela Priya
%T Article:Pass-Phrase based Speaker Identification
%J International Journal of Computer Applications
%@ 0975-8887
%V 10
%N 8
%P 6-9
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The problem of speaker identification is an area with many different applications. The most practical use can be found in applications dealing with security, surveillance, and automatic transcription in a multi-speaker environment. Speaker identification is a difficult task and the task has several different approaches. The state of the art for speaker identification techniques include Dynamic Time Warped (DTW) template matching, Hidden Markov Modeling (HMM), and codebook schemes based on Vector Quantization (VQ). This paper emphasizes on text dependent speaker identification, which deals with detecting a particular speaker from a known population. The system reads the speech utterance. System identifies the user by comparing the codebook of speech utterance with those of the stored in the database and lists, which contain the most likely speakers, could have given that speech utterance. The vector quantization approach will be proposed, due to ease of implementation and high accuracy.

References
  1. F. Bimbot, L. Mathan, A. de Lima and G. Chollet, “Standard and target driven AR-vector models for speech analysis and speaker recognition,” in IEEE ICASSP, vol. 2, 1992, pp. 5–8.
  2. J. de Veth and H. Bourlard, “Comparison of hidden Markov model techniques for automatic speaker verification in real- world conditions,” Speech Communication, vol. 17, Mar. 1995, pp. 81–90.
  3. D. Reynolds and R. Rose, “Robust text-independent speaker identification using Gaussian mixture speaker models,” IEEE Trans. Speech Audio Processing, vol. 3, Jan. 1995, pp. 72-83.
  4. M.W. Mak, W.G. Allen, and G.G. Sexton, “Speaker identification using multilayer perceptron and radial basis function networks,” Neurocomputing, vol. 6, no. 1, 1994, pp. 99-117.
  5. Z.X. Yuan, B.L. Xu, and C.Z. Yu, “A kind of fuzzy Neural networks for text-independent speaker identification,” in Proc. IEEE Int. Confe. Acoustics, Speech, Signal Processing, 1996, pp.657-660.
  6. Brian J,Jennifer Vining ” Automatic Speaker Recognition Using Neural Networks” ,2004
  7. M.W. Mak, W.G. Allen, and G.G. Sexton, “Speaker identification using multilayer perceptron and radial basis function networks,” Neurocomputing, vol. 6, no. 1, 1994, pp. 99-117.
  8. A. Gersho, R. Gray, “Vector Quantization and Signal Compression”, Kluwer Academic Publishers, Boston, 1992.
  9. Frederic Bimbot “A Tutorial on Text-Independent Speaker Verification”, EURASIP Journal on Applied Signal Processing 2004:4, 430–451
  10. M. Przybocki and A. Martin, “The 1999 NIST speaker recognition evaluation, using summed two-channel telephone data for speaker detection and speaker tracking,” in Proc. European Conference on Speech Communication and Technology (Eurospeech ’99), vol. 5, pp. 2215–2218, Budpest, Hungary, September 1999
Index Terms

Computer Science
Information Sciences

Keywords

Biometrics Speaker Identification LPC Mel Cepstrum HMM VQ Codebook