{"title":"语音识别的自适应模板方法","authors":"Y. Liu, Y. Lee, H. Chen, G. Sun","doi":"10.1109/NNSP.1992.253703","DOIUrl":null,"url":null,"abstract":"An adaptive template method for pattern recognition is proposed. The template adaptation algorithm is derived based on minimizing the classification error of the classifier. The authors have applied this method to a multispeaker English E-set recognition experiment and achieved a 90.38% average recognition rate with only one template for each letter. This indicates that the derived templates are able to capture the speaker-invariant features of speech signals.<<ETX>>","PeriodicalId":438250,"journal":{"name":"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Adaptive template method for speech recognition\",\"authors\":\"Y. Liu, Y. Lee, H. Chen, G. Sun\",\"doi\":\"10.1109/NNSP.1992.253703\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An adaptive template method for pattern recognition is proposed. The template adaptation algorithm is derived based on minimizing the classification error of the classifier. The authors have applied this method to a multispeaker English E-set recognition experiment and achieved a 90.38% average recognition rate with only one template for each letter. This indicates that the derived templates are able to capture the speaker-invariant features of speech signals.<<ETX>>\",\"PeriodicalId\":438250,\"journal\":{\"name\":\"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.1992.253703\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.1992.253703","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An adaptive template method for pattern recognition is proposed. The template adaptation algorithm is derived based on minimizing the classification error of the classifier. The authors have applied this method to a multispeaker English E-set recognition experiment and achieved a 90.38% average recognition rate with only one template for each letter. This indicates that the derived templates are able to capture the speaker-invariant features of speech signals.<>
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