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Trends in Applied Sciences Research
  Year: 2014 | Volume: 9 | Issue: 8 | Page No.: 472-479
DOI: 10.3923/tasr.2014.472.479
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Speaker Identification Using Bayesian Algorithm

Hasan Bjaili, Khalid Daqrouq and Rami Al-Hmouz

The majority of the computations during the process of speaker identification originate from the likelihood computations between the feature vectors of the unknown speaker and the models in the database. The identification time depends on the number of feature vectors, their dimensionality, the complexity of the speaker models and the number of speakers. The main objective of this study is to use Bayesian algorithm in speaker identification with different features extraction methods. Three methods are used to extract the essential speaker features based on Discrete Wavelet Transform and Linear Prediction Coefficient (LPC). The results showed that Bayesian algorithm gives excellent performance in case of minimum signal fluctuations. It has been shown that Bayesian classifier achieves a better recognition rate (90.93%) with the Wavelet Packet (WP) and Average Framing Linear Predication Coding (AFLPC) feature extraction method. It is also suggested to analyze the proposed system in Additive White Gaussian Noise (AWGN) and real noise environments; 58.56% for 0 dB and 70.52% for 5 dB.
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How to cite this article:

Hasan Bjaili, Khalid Daqrouq and Rami Al-Hmouz, 2014. Speaker Identification Using Bayesian Algorithm. Trends in Applied Sciences Research, 9: 472-479.

DOI: 10.3923/tasr.2014.472.479






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