{"title":"在对话中建模多模态用户ID","authors":"H. Holzapfel, A. Waibel","doi":"10.1109/SLT.2008.4777853","DOIUrl":null,"url":null,"abstract":"This paper presents an approach to model user ID in dialogue. A belief network is used to integrate ID classifiers, such as face ID and voice ID, and person related information, such as the first name and last name of a person from speech recognition or spelling. Different network structures are analyzed and compared with each other and are compared with a rule-based user model. The approach is evaluated on dialogue data collected in a person identification scenario, which includes both, identification of known persons and interactive learning of names and ID of unknown persons.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Modelling multimodal user ID in dialogue\",\"authors\":\"H. Holzapfel, A. Waibel\",\"doi\":\"10.1109/SLT.2008.4777853\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an approach to model user ID in dialogue. A belief network is used to integrate ID classifiers, such as face ID and voice ID, and person related information, such as the first name and last name of a person from speech recognition or spelling. Different network structures are analyzed and compared with each other and are compared with a rule-based user model. The approach is evaluated on dialogue data collected in a person identification scenario, which includes both, identification of known persons and interactive learning of names and ID of unknown persons.\",\"PeriodicalId\":186876,\"journal\":{\"name\":\"2008 IEEE Spoken Language Technology Workshop\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE Spoken Language Technology Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SLT.2008.4777853\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE Spoken Language Technology Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SLT.2008.4777853","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents an approach to model user ID in dialogue. A belief network is used to integrate ID classifiers, such as face ID and voice ID, and person related information, such as the first name and last name of a person from speech recognition or spelling. Different network structures are analyzed and compared with each other and are compared with a rule-based user model. The approach is evaluated on dialogue data collected in a person identification scenario, which includes both, identification of known persons and interactive learning of names and ID of unknown persons.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
