{"title":"Speaker-dependent 1000 word recognition using a large scale neural network 'CombNET-II' and dynamic spectral features","authors":"T. Kitamura, W. Hui, A. Iwata, N. Suzumura","doi":"10.1109/IJCNN.1991.170560","DOIUrl":null,"url":null,"abstract":"The authors describe speaker-dependent large vocabulary word recognition using a large-scale neural network, CombNET-II, which consists of a four-layered neural network with a comb structure, and dynamic spectral features of speech based on a two-dimensional mel-cepstrum. CombNET-II consists of two types of neural networks. The first part is a stem network which learns by a self-growing algorithm and roughly classifies an input pattern. The second part consists of many branch networks which learn by a backpropagation algorithm and precisely classify the input pattern. A stem network is a vector quantizing network and it reduces the number of category candidates for the branch networks, so that each branch network has only a small number of connections and it is easy to tune up. Experiments on speaker-dependent large-vocabulary word recognition for 1000 Chinese spoken words is described. Experimental results show that the high recognition accuracy of 99.1% is obtained and that CombNET-II is very effective for large vocabulary spoken word recognition.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170560","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The authors describe speaker-dependent large vocabulary word recognition using a large-scale neural network, CombNET-II, which consists of a four-layered neural network with a comb structure, and dynamic spectral features of speech based on a two-dimensional mel-cepstrum. CombNET-II consists of two types of neural networks. The first part is a stem network which learns by a self-growing algorithm and roughly classifies an input pattern. The second part consists of many branch networks which learn by a backpropagation algorithm and precisely classify the input pattern. A stem network is a vector quantizing network and it reduces the number of category candidates for the branch networks, so that each branch network has only a small number of connections and it is easy to tune up. Experiments on speaker-dependent large-vocabulary word recognition for 1000 Chinese spoken words is described. Experimental results show that the high recognition accuracy of 99.1% is obtained and that CombNET-II is very effective for large vocabulary spoken word recognition.<>