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Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop最新文献

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Word recognition based on the combination of a sequential neural network and the GPDM discriminative training algorithm 基于序列神经网络和GPDM判别训练算法相结合的词识别
Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop Pub Date : 1991-06-01 DOI: 10.1109/NNSP.1991.239504
Wen-Yuan Chen, Sin-Horng Chen
{"title":"Word recognition based on the combination of a sequential neural network and the GPDM discriminative training algorithm","authors":"Wen-Yuan Chen, Sin-Horng Chen","doi":"10.1109/NNSP.1991.239504","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239504","url":null,"abstract":"The authors propose an isolated-word recognition method based on the combination of a sequential neural network and a discriminative training algorithm using the Generalized Probabilistic Descent Method (GPDM). The sequential neural network deals with the temporal variation of speech by dynamic programming, and the GPDM discriminative training algorithm is used to discriminate easily confused words by enhancing the distinguishing sounds of them during the scoring procedure. A Mandarin digit database uttered by 100 speakers was used to evaluate the performance of this method. The recognition rates are 99.1% on training data and 96.3% on testing data.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115318268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
A mapping approach for designing neural sub-nets 一种设计神经网络的映射方法
Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop Pub Date : 1900-01-01 DOI: 10.1109/NNSP.1991.239534
K. Rohani, M.-S. Chen, M. Manry
{"title":"A mapping approach for designing neural sub-nets","authors":"K. Rohani, M.-S. Chen, M. Manry","doi":"10.1109/NNSP.1991.239534","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239534","url":null,"abstract":"Several investigators have constructed back-propagation (BP) neural networks by assembling smaller, pre-trained building blocks. This approach leads to faster training and provides a known topology for the network. The authors carry this process down one additional level, by describing methods for mapping given functions to sub-blocks. First, polynomial approximations to the desired function are found. Then the polynomial is mapped to a BP network, using an extension of a constructive proof to universal approximation. Examples are given to illustrate the method.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116001756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Shape recognition with nearest neighbor isomorphic network 基于最近邻同构网络的形状识别
Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop Pub Date : 1900-01-01 DOI: 10.1109/NNSP.1991.239517
H. Yau, M. Manry
{"title":"Shape recognition with nearest neighbor isomorphic network","authors":"H. Yau, M. Manry","doi":"10.1109/NNSP.1991.239517","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239517","url":null,"abstract":"The nearest neighbor isomorphic network paradigm is a combination of sigma-pi units in the hidden layer and product units in the output layer. Good initial weights can be found through clustering of the input training vectors, and the network can be successfully trained via backpropagation learning. The authors show theoretical conditions under which the product operation can replace the Min operation. Advantages to the product operation are summarized. Under some sufficient conditions, the product operation yields the same classification results as the Min operation. They apply their algorithm to a geometric shape recognition problem and compare the performances with those of two other well-known algorithms.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122232567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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