{"title":"进化辨别说话人适应","authors":"S. Selouani","doi":"10.1109/ASRU.2011.6163924","DOIUrl":null,"url":null,"abstract":"This paper presents a new evolutionary-based approach that aims at investigating more solutions while simplifying the speaker adaptation process. In this approach, a single global transformation set of parameters is optimized by genetic algorithms using a discriminative objective function. The goal is to achieve accurate speaker adaptation whatever the amount of available adaptive data. Experiments using the ARPA-RM database demonstrate the effectiveness of the proposed method.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Evolutionary discriminative speaker adaptation\",\"authors\":\"S. Selouani\",\"doi\":\"10.1109/ASRU.2011.6163924\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new evolutionary-based approach that aims at investigating more solutions while simplifying the speaker adaptation process. In this approach, a single global transformation set of parameters is optimized by genetic algorithms using a discriminative objective function. The goal is to achieve accurate speaker adaptation whatever the amount of available adaptive data. Experiments using the ARPA-RM database demonstrate the effectiveness of the proposed method.\",\"PeriodicalId\":338241,\"journal\":{\"name\":\"2011 IEEE Workshop on Automatic Speech Recognition & Understanding\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE Workshop on Automatic Speech Recognition & Understanding\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASRU.2011.6163924\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASRU.2011.6163924","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a new evolutionary-based approach that aims at investigating more solutions while simplifying the speaker adaptation process. In this approach, a single global transformation set of parameters is optimized by genetic algorithms using a discriminative objective function. The goal is to achieve accurate speaker adaptation whatever the amount of available adaptive data. Experiments using the ARPA-RM database demonstrate the effectiveness of the proposed method.