TY - GEN
T1 - Driver voice identification system using auto-correlation function and average magnitude difference function
AU - Wu, Jian Da
AU - Liu, Pang Yi
AU - Hong, Guan Long
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84893944197&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893944197&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMM.490-491.1287
DO - 10.4028/www.scientific.net/AMM.490-491.1287
M3 - Conference contribution
AN - SCOPUS:84893944197
SN - 9783038350019
T3 - Applied Mechanics and Materials
SP - 1287
EP - 1292
BT - Mechanical Design and Power Engineering
T2 - 2013 2nd International Conference on Mechanical Design and Power Engineering, ICMDPE 2013
Y2 - 29 November 2013 through 30 November 2013
ER -