{"title":"使用大规模神经网络“CombNET-II”和动态光谱特征的讲话者依赖的1000字识别","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":"{\"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}","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}
Speaker-dependent 1000 word recognition using a large scale neural network 'CombNET-II' and dynamic spectral features
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.<>