{"title":"两种编程环境下基于i向量的说话人识别器的开发","authors":"Maroš Jakubec, Eva Lieskovská, R. Jarina","doi":"10.1109/NTSP49686.2020.9229552","DOIUrl":null,"url":null,"abstract":"The i-vectors with Probabilistic Linear Discriminative Analysis (PLDA) are known to be one of the latest and most advanced techniques in the field of Automatic Speaker Recognition (ASR). The paper focuses on the development of i-vector/PLDA based the ASR systems in two programming environments, namely Python and MATLAB, which are popular among machine-learning community. Comparative evaluation of system performance, in terms of accuracy and computational requirements, for both platforms is presented.","PeriodicalId":197079,"journal":{"name":"2020 New Trends in Signal Processing (NTSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of Speaker Recognizer Using I-vectors in Two Programming Environments\",\"authors\":\"Maroš Jakubec, Eva Lieskovská, R. Jarina\",\"doi\":\"10.1109/NTSP49686.2020.9229552\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The i-vectors with Probabilistic Linear Discriminative Analysis (PLDA) are known to be one of the latest and most advanced techniques in the field of Automatic Speaker Recognition (ASR). The paper focuses on the development of i-vector/PLDA based the ASR systems in two programming environments, namely Python and MATLAB, which are popular among machine-learning community. Comparative evaluation of system performance, in terms of accuracy and computational requirements, for both platforms is presented.\",\"PeriodicalId\":197079,\"journal\":{\"name\":\"2020 New Trends in Signal Processing (NTSP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 New Trends in Signal Processing (NTSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NTSP49686.2020.9229552\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 New Trends in Signal Processing (NTSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NTSP49686.2020.9229552","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of Speaker Recognizer Using I-vectors in Two Programming Environments
The i-vectors with Probabilistic Linear Discriminative Analysis (PLDA) are known to be one of the latest and most advanced techniques in the field of Automatic Speaker Recognition (ASR). The paper focuses on the development of i-vector/PLDA based the ASR systems in two programming environments, namely Python and MATLAB, which are popular among machine-learning community. Comparative evaluation of system performance, in terms of accuracy and computational requirements, for both platforms is presented.
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