{"title":"On the speaker verification using the TESPAR coding method","authors":"E. Lupu, Z. Fehér, P. Pop","doi":"10.1109/SCS.2003.1226976","DOIUrl":null,"url":null,"abstract":"This work describes study on the speaker verification rate, using the TESPAR (Time Encoding Signal Processing and Recognition) coding method, when the speech signal is sampled at different rates. The effect of filtering on the speech signal was studied, as well. The TESPAR method is a processing and recognition method in the time domain, proposed by R.A. King. The key problem is to define the TESPAR alphabet used for the TESPAR coding process. In this paper is proposed an approach to generate this alphabet using the Kohonen Neural Networks in a vector quantization process. For the recognition process parallel Multi Layer Perceptron (MLP) neural network were used. As inputs for training/test vectors of the MLP-NN, the TESPAR-S matrices were employed.","PeriodicalId":375963,"journal":{"name":"Signals, Circuits and Systems, 2003. SCS 2003. International Symposium on","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signals, Circuits and Systems, 2003. SCS 2003. International Symposium on","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCS.2003.1226976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
This work describes study on the speaker verification rate, using the TESPAR (Time Encoding Signal Processing and Recognition) coding method, when the speech signal is sampled at different rates. The effect of filtering on the speech signal was studied, as well. The TESPAR method is a processing and recognition method in the time domain, proposed by R.A. King. The key problem is to define the TESPAR alphabet used for the TESPAR coding process. In this paper is proposed an approach to generate this alphabet using the Kohonen Neural Networks in a vector quantization process. For the recognition process parallel Multi Layer Perceptron (MLP) neural network were used. As inputs for training/test vectors of the MLP-NN, the TESPAR-S matrices were employed.
本文采用TESPAR (Time Encoding Signal Processing and Recognition,时间编码信号处理与识别)编码方法,对语音信号在不同采样率下的说话人验证率进行了研究。研究了滤波对语音信号的影响。TESPAR方法是由R.A. King提出的一种时域处理和识别方法。关键问题是定义用于TESPAR编码过程的TESPAR字母表。本文提出了一种利用Kohonen神经网络在矢量量化过程中生成该字母表的方法。在识别过程中,采用并行多层感知器(MLP)神经网络。采用TESPAR-S矩阵作为MLP-NN的训练/测试向量输入。