Driver voice identification system using auto-correlation function and average magnitude difference function

Jian Da Wu, Pang Yi Liu, Guan Long Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This study presents a driver identification system using voice analysis for a vehicle security system. The structure of the proposed system has three parts. The first procedure is speech pre-processing, the second is feature extraction of sound signals, and the third is classification of driver voice. Initially, a database of sound signals for several drivers was established. The volume and zero-crossing rate (ZCR) of sound are used to detect the voice end-point in order to reduce data computation. Then the Auto-correlation Function (ACF) and Average Magnitude Difference Function (AMDF) methods are applied to retrieve the voice pitch features. Finally these features are used to identify the drivers by a General Regression Neural Network (GRNN). The experimental results show that the development of this voice identification system can use fewer feature vectors of pitch to obtain a good recognition rate.

Original languageEnglish
Title of host publicationMechanical Design and Power Engineering
Pages1287-1292
Number of pages6
DOIs
Publication statusPublished - 2014
Event2013 2nd International Conference on Mechanical Design and Power Engineering, ICMDPE 2013 - Beijing, China
Duration: 2013 Nov 292013 Nov 30

Publication series

NameApplied Mechanics and Materials
Volume490-491
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Conference

Conference2013 2nd International Conference on Mechanical Design and Power Engineering, ICMDPE 2013
CountryChina
CityBeijing
Period13-11-2913-11-30

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Wu, J. D., Liu, P. Y., & Hong, G. L. (2014). Driver voice identification system using auto-correlation function and average magnitude difference function. In Mechanical Design and Power Engineering (pp. 1287-1292). (Applied Mechanics and Materials; Vol. 490-491). https://doi.org/10.4028/www.scientific.net/AMM.490-491.1287