{"title":"基于HMM和神经网络的实时自动语音识别","authors":"Y. Arriola, Rolando Carrasco","doi":"10.1109/ITS.1990.175597","DOIUrl":null,"url":null,"abstract":"The authors describe the implementation of an automatic speech recognition system for isolated words, based on the integration of neural networks (NN) for phonetic mapping with hidden Markov models (HMMs) for word modeling. The main system features and implementation algorithms are presented, and initial experimental results and software capabilities are shown. It is shown that the inclusion of the NN model between the acoustic processor and the HMM improves the recognition and avoids the clustering and labelling phases.<<ETX>>","PeriodicalId":405932,"journal":{"name":"SBT/IEEE International Symposium on Telecommunications","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1990-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Real-time automatic speech recognition using HMM and neural networks\",\"authors\":\"Y. Arriola, Rolando Carrasco\",\"doi\":\"10.1109/ITS.1990.175597\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors describe the implementation of an automatic speech recognition system for isolated words, based on the integration of neural networks (NN) for phonetic mapping with hidden Markov models (HMMs) for word modeling. The main system features and implementation algorithms are presented, and initial experimental results and software capabilities are shown. It is shown that the inclusion of the NN model between the acoustic processor and the HMM improves the recognition and avoids the clustering and labelling phases.<<ETX>>\",\"PeriodicalId\":405932,\"journal\":{\"name\":\"SBT/IEEE International Symposium on Telecommunications\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1990-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SBT/IEEE International Symposium on Telecommunications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITS.1990.175597\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SBT/IEEE International Symposium on Telecommunications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITS.1990.175597","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-time automatic speech recognition using HMM and neural networks
The authors describe the implementation of an automatic speech recognition system for isolated words, based on the integration of neural networks (NN) for phonetic mapping with hidden Markov models (HMMs) for word modeling. The main system features and implementation algorithms are presented, and initial experimental results and software capabilities are shown. It is shown that the inclusion of the NN model between the acoustic processor and the HMM improves the recognition and avoids the clustering and labelling phases.<>