A speaker identification system using a model of artificial neural networks for an elevator application

被引：14
|
作者：
Adami, AG
Barone, DAC
机构：
[1] Univ Caxias Do Sul, Dept Informat, BR-95070560 Caxias Do Sul, RS, Brazil
[2] Univ Fed Rio Grande Sul, Inst Informat, BR-91000000 Porto Alegre, RS, Brazil
来源：
INFORMATION SCIENCES | 2001年 / 138卷 / 1-4期
关键词：
D O I：
10.1016/S0020-0255(01)00129-3
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a comparison of some features for speaker identification applied to a building security system. The features used in this paper are pitch, frequency formants, linear predictive coding (LPC) coefficients and cepstral coefficients computed from LPC. The comparison was based on a system for building security that uses the voice of the residents to control the access to the building. The system uses a model of artificial neural network called multi-layer perceptron (MLP) as a classifier. This paper shows that cepstral coefficients are more efficient than LPC coefficients for the security system. (C) 2001 Elsevier Science Inc. All rights reserved.
引用
收藏
页码：1 / 5
页数：5
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