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

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.

[1]  Hervé Bourlard,et al.  Comparison of hidden Markov model techniques for automatic speaker verification in real-world conditions , 1995, Speech Commun..

[2]  Vincent G. Duffy,et al.  Voice recognition based human-computer interface design , 1999 .

[3]  Zhigang Cao,et al.  Improved MFCC-based feature for robust speaker identification , 2005 .

[4]  Jie Zhou,et al.  Fingerprint recognition using model-based density map , 2006, IEEE Transactions on Image Processing.

[5]  Shung-Yung Lung.  Wavelet feature selection based neural networks with application to the text independent speaker identification , 2006, Pattern Recognit..

[6]  Olivier Rioul,et al.  Fast algorithms for discrete and continuous wavelet transforms , 1992, IEEE Trans. Inf. Theory.

[7]  H. Zheng,et al.  GEAR FAULT DIAGNOSIS BASED ON CONTINUOUS WAVELET TRANSFORM , 2002 .

[8]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Toby Berger,et al.  Efficient text-independent speaker verification with structural Gaussian mixture models and neural network , 2003, IEEE Trans. Speech Audio Process..

[10]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[11]  Krzysztof Wilde,et al.  Application of continuous wavelet transform in vibration based damage detection method for beams and plates , 2006 .

[12]  Douglas A. Reynolds,et al.  Speaker identification and verification using Gaussian mixture speaker models , 1995, Speech Commun..

[13]  Yiu Sang Moon,et al.  Fast fingerprint verification using subregions of fingerprint images , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[15]  Li Li,et al.  Haar wavelet for machine fault diagnosis , 2007 .

[16]  John J. Hopfield,et al.  Connected-digit speaker-dependent speech recognition using a neural network with time-delayed connections , 1991, IEEE Trans. Signal Process..

[17]  Stefan R Schweinberger,et al.  Human brain potential correlates of voice priming and voice recognition , 2001, Neuropsychologia.

[18]  Yongsheng Gao,et al.  Face Recognition Using Line Edge Map , 2002, IEEE Trans. Pattern Anal. Mach. Intell..