基于人工神经网络模型的说话人识别住宅安防原型特征比较

A. Adami, G.B. Lazzarotto, E.F. Foppa, D.A. Couto Barone
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引用次数: 1

摘要

本文介绍了一种基于扬声器识别的电梯安全系统的实现。该系统使用一种称为多层感知器的人工神经网络模型作为分类器。在这项工作中,利用一些特征,如基音、共振峰、感知线性预测系数、梅尔-倒谱系数和倒谱系数,在分类过程中获得最佳结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comparison between features for a residential security prototype based on speaker identification with a model of artificial neural network
This paper presents an implementation of a security system for elevators using speaker identification. The system uses a model of an artificial neural network called a multi-layer perceptron as a classifier. In this work, some features, such as pitch, formants, perceptual linear prediction coefficients, mel-cepstral coefficients and cepstral coefficients, are used to obtain the best results in the classification process.
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