A. Stefanidi, A. Priorov, A. Topnikov, Ekaterina Sidorova
{"title":"基于叠加弱分类器的改进语音活动检测器","authors":"A. Stefanidi, A. Priorov, A. Topnikov, Ekaterina Sidorova","doi":"10.1109/SIBCON56144.2022.10002950","DOIUrl":null,"url":null,"abstract":"The article considers the problem of speech fragments extraction. The authors have brought together a dataset of speech signals VADSpeakersDB. This dataset consists of phonograms recorded with the help of an application for video conferences. The research considers an original algorithm based on the stacking of independent speech activity detectors and compares it with traditional approaches. The solution has a high accuracy of detecting voice fragments-more than 90%.","PeriodicalId":265523,"journal":{"name":"2022 International Siberian Conference on Control and Communications (SIBCON)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved Voice Activity Detector Based on Stacking Weak Classifiers\",\"authors\":\"A. Stefanidi, A. Priorov, A. Topnikov, Ekaterina Sidorova\",\"doi\":\"10.1109/SIBCON56144.2022.10002950\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article considers the problem of speech fragments extraction. The authors have brought together a dataset of speech signals VADSpeakersDB. This dataset consists of phonograms recorded with the help of an application for video conferences. The research considers an original algorithm based on the stacking of independent speech activity detectors and compares it with traditional approaches. The solution has a high accuracy of detecting voice fragments-more than 90%.\",\"PeriodicalId\":265523,\"journal\":{\"name\":\"2022 International Siberian Conference on Control and Communications (SIBCON)\",\"volume\":\"82 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Siberian Conference on Control and Communications (SIBCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIBCON56144.2022.10002950\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Siberian Conference on Control and Communications (SIBCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIBCON56144.2022.10002950","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved Voice Activity Detector Based on Stacking Weak Classifiers
The article considers the problem of speech fragments extraction. The authors have brought together a dataset of speech signals VADSpeakersDB. This dataset consists of phonograms recorded with the help of an application for video conferences. The research considers an original algorithm based on the stacking of independent speech activity detectors and compares it with traditional approaches. The solution has a high accuracy of detecting voice fragments-more than 90%.
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