Improvement Of The Text Dependent Speaker Identification System Using Discrete MMM With Cepstral Based Features

Authors

  • Md Rabiul Islam Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology (RUET), Rajshahi
  • Md Fayzur Rahman Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology (RUET), Rajshahi
  • Muhammad Abdul Goffar Khan Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology (RUET), Rajshahi

DOI:

https://doi.org/10.3329/diujst.v6i2.9341

Keywords:

Biometric Technologies, Automatic Speaker Identification, Cepstral Coefficients, Feature Extraction, Hidden Markov Model.

Abstract

In this paper, an improved strategy for automated text based speaker identification scheme has been proposed. The identification process incorporates the Hidden Markov Model technique. After preprocessing the speech, HMM is used in the learning and identification. Features are extracted by different techniques such as RCC, MFCC, ΔMFCC, ΔΔMFCC, LPC and LPCC which is almost different in each case. The highest identification rate of 93% has been achieved in the close set text dependent speaker identification system.

Keywords: Biometric Technologies; Automatic Speaker Identification; Cepstral Coefficients; Feature Extraction; Hidden Markov Model.

DOI: http://dx.doi.org/10.3329/diujst.v6i2.9341 DIUJST 2011; 6(2): 14-21

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How to Cite

Islam, M. R., Rahman, M. F., & Khan, M. A. G. (2011). Improvement Of The Text Dependent Speaker Identification System Using Discrete MMM With Cepstral Based Features. Daffodil International University Journal of Science and Technology, 6(2), 14–21. https://doi.org/10.3329/diujst.v6i2.9341

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Papers