{"title":"对话的语义分析:会议中说话人的实时识别系统","authors":"Oriol Vinyals, G. Friedland","doi":"10.1109/ICSC.2008.58","DOIUrl":null,"url":null,"abstract":"In the following article we present an application that enables online identification of who is currently speaking using a single farfield microphone in a meeting scenario. By leveraging techniques from both the field of speaker identification and speaker diarization, the system is able to recognize the current speaker after any two seconds of speech. An evaluation of the robustness of the algorithm using the AMI meeting corpus and the NIST speaker diarization development set resulted in a diarization error rate of 12.67%.","PeriodicalId":102805,"journal":{"name":"2008 IEEE International Conference on Semantic Computing","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":"{\"title\":\"Towards Semantic Analysis of Conversations: A System for the Live Identification of Speakers in Meetings\",\"authors\":\"Oriol Vinyals, G. Friedland\",\"doi\":\"10.1109/ICSC.2008.58\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the following article we present an application that enables online identification of who is currently speaking using a single farfield microphone in a meeting scenario. By leveraging techniques from both the field of speaker identification and speaker diarization, the system is able to recognize the current speaker after any two seconds of speech. An evaluation of the robustness of the algorithm using the AMI meeting corpus and the NIST speaker diarization development set resulted in a diarization error rate of 12.67%.\",\"PeriodicalId\":102805,\"journal\":{\"name\":\"2008 IEEE International Conference on Semantic Computing\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"28\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Conference on Semantic Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSC.2008.58\",\"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 International Conference on Semantic Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSC.2008.58","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards Semantic Analysis of Conversations: A System for the Live Identification of Speakers in Meetings
In the following article we present an application that enables online identification of who is currently speaking using a single farfield microphone in a meeting scenario. By leveraging techniques from both the field of speaker identification and speaker diarization, the system is able to recognize the current speaker after any two seconds of speech. An evaluation of the robustness of the algorithm using the AMI meeting corpus and the NIST speaker diarization development set resulted in a diarization error rate of 12.67%.
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