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 language | English |
|---|---|
| Title of host publication | Mechanical Design and Power Engineering |
| Pages | 1287-1292 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2014 Feb 20 |
| Event | 2013 2nd International Conference on Mechanical Design and Power Engineering, ICMDPE 2013 - Beijing, China Duration: 2013 Nov 29 → 2013 Nov 30 |
Publication series
| Name | Applied Mechanics and Materials |
|---|---|
| Volume | 490-491 |
| ISSN (Print) | 1660-9336 |
| ISSN (Electronic) | 1662-7482 |
Conference
| Conference | 2013 2nd International Conference on Mechanical Design and Power Engineering, ICMDPE 2013 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 13-11-29 → 13-11-30 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
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Driver voice identification system using auto-correlation function and average magnitude difference function. / Wu, Jian-Da; Liu, Pang Yi; Hong, Guan Long.
Mechanical Design and Power Engineering. 2014. p. 1287-1292 (Applied Mechanics and Materials; Vol. 490-491).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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
PY - 2014/2/20
Y1 - 2014/2/20
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
ER -