Fernando Batista, R. Amaral, I. Trancoso, N. Mamede
{"title":"动态模型自适应对语音识别的影响","authors":"Fernando Batista, R. Amaral, I. Trancoso, N. Mamede","doi":"10.1109/SLT.2008.4777894","DOIUrl":null,"url":null,"abstract":"The application of speech recognition to live subtitling of Broadcast News has motivated the adaptation of the lexical and language models of the recognizer on a daily basis with text material retrieved from online newspapers. This paper studies the impact of this adaptation on two of the blocks following the speech recognition module: capitalization and topic indexation. We describe and evaluate different adaptation approaches that try to explore the language dynamics.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"515 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Impact of dynamic model adaptation beyond speech recognition\",\"authors\":\"Fernando Batista, R. Amaral, I. Trancoso, N. Mamede\",\"doi\":\"10.1109/SLT.2008.4777894\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The application of speech recognition to live subtitling of Broadcast News has motivated the adaptation of the lexical and language models of the recognizer on a daily basis with text material retrieved from online newspapers. This paper studies the impact of this adaptation on two of the blocks following the speech recognition module: capitalization and topic indexation. We describe and evaluate different adaptation approaches that try to explore the language dynamics.\",\"PeriodicalId\":186876,\"journal\":{\"name\":\"2008 IEEE Spoken Language Technology Workshop\",\"volume\":\"515 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE Spoken Language Technology Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SLT.2008.4777894\",\"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.2008.4777894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Impact of dynamic model adaptation beyond speech recognition
The application of speech recognition to live subtitling of Broadcast News has motivated the adaptation of the lexical and language models of the recognizer on a daily basis with text material retrieved from online newspapers. This paper studies the impact of this adaptation on two of the blocks following the speech recognition module: capitalization and topic indexation. We describe and evaluate different adaptation approaches that try to explore the language dynamics.
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