Call for Paper - April 2023 Edition
IJCA solicits original research papers for the April 2023 Edition. Last date of manuscript submission is March 20, 2023. Read More

Pass-Phrase based Speaker Identification

International Journal of Computer Applications
© 2010 by IJCA Journal
Number 8 - Article 2
Year of Publication: 2010
I.Leela Priya

Y.K.Viswanadham, T.V.Subrahmanyam and I.Leela Priya. Article:Pass-Phrase based Speaker Identification. International Journal of Computer Applications 10(8):6–9, November 2010. Published By Foundation of Computer Science. BibTeX

	author = {Y.K.Viswanadham and T.V.Subrahmanyam and I.Leela Priya},
	title = {Article:Pass-Phrase based Speaker Identification},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {10},
	number = {8},
	pages = {6--9},
	month = {November},
	note = {Published By Foundation of Computer Science}


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.


  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Brian J,Jennifer Vining ” Automatic Speaker Recognition Using Neural Networks” ,2004
  • 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.
  • A. Gersho, R. Gray, “Vector Quantization and Signal Compression”, Kluwer Academic Publishers, Boston, 1992.
  • Frederic Bimbot “A Tutorial on Text-Independent Speaker Verification”, EURASIP Journal on Applied Signal Processing 2004:4, 430–451
  • 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