Driver identification based on voice signal using continuous wavelet transform and artificial neural network techniques

Jian-Da Wu, Siou Huan Ye

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

This paper presents a study of driver's voice feature selection and classification for speaker identification in a vehicle security system. The proposed system consisted of a combination of feature extraction using continuous wavelet technique and voice classification using artificial neural network. In the feature extraction, a time-averaged wavelet spectrum based on continuous wavelet transform is proposed. Meanwhile, the artificial neural network techniques were used for classification in the proposed system. In order to verify the effect of the proposed system for classification, a conventional back-propagation neural network (BPNN) and generalized regression neural network (GRNN) were used and compared in the experimental investigation. The experimental results demonstrated the effectiveness of the proposed speaker identification system. The identification rate is about 92% for using BPNN and 97% for using GRNN approach.

Original languageEnglish
Pages (from-to)1061-1069
Number of pages9
JournalExpert Systems with Applications
Volume36
Issue number2 PART 1
DOIs
Publication statusPublished - 2009 Jan 1

Fingerprint

Wavelet transforms
Neural networks
Feature extraction
Backpropagation
Identification (control systems)
Security systems

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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abstract = "This paper presents a study of driver's voice feature selection and classification for speaker identification in a vehicle security system. The proposed system consisted of a combination of feature extraction using continuous wavelet technique and voice classification using artificial neural network. In the feature extraction, a time-averaged wavelet spectrum based on continuous wavelet transform is proposed. Meanwhile, the artificial neural network techniques were used for classification in the proposed system. In order to verify the effect of the proposed system for classification, a conventional back-propagation neural network (BPNN) and generalized regression neural network (GRNN) were used and compared in the experimental investigation. The experimental results demonstrated the effectiveness of the proposed speaker identification system. The identification rate is about 92{\%} for using BPNN and 97{\%} for using GRNN approach.",
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Driver identification based on voice signal using continuous wavelet transform and artificial neural network techniques. / Wu, Jian-Da; Ye, Siou Huan.

In: Expert Systems with Applications, Vol. 36, No. 2 PART 1, 01.01.2009, p. 1061-1069.

Research output: Contribution to journalArticle

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