Hung Van Dinh, Tuan Van Mai, Quyen B. Dam, Bao Quoc Nguyen
{"title":"SV - VLSP2021: The Smartcall - ITS’s Systems","authors":"Hung Van Dinh, Tuan Van Mai, Quyen B. Dam, Bao Quoc Nguyen","doi":"10.25073/2588-1086/vnucsce.339","DOIUrl":null,"url":null,"abstract":"This paper presents the Smartcall - ITS’s systems submitted to the Vietnamese Language and Speech Processing, Speaker Verification (SV) task. The challenge consists of two tasks focusing on the development of SV models with limited data and testing the robustness of SV systems. In both tasks, we used various pre-trained speaker embedding models with different architectures: TDNN, Resnet34. After a specific fine-tuning strategy with data from the organiser, our system achieved the first rank for both two tasks with the Equal Error Rate respectively are 1.755%, 1.95%. In this paper, we describe our system developed for the booth two tasks in the VLSP2021 Speaker Verification shared-task.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"VNU Journal of Science: Computer Science and Communication Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25073/2588-1086/vnucsce.339","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents the Smartcall - ITS’s systems submitted to the Vietnamese Language and Speech Processing, Speaker Verification (SV) task. The challenge consists of two tasks focusing on the development of SV models with limited data and testing the robustness of SV systems. In both tasks, we used various pre-trained speaker embedding models with different architectures: TDNN, Resnet34. After a specific fine-tuning strategy with data from the organiser, our system achieved the first rank for both two tasks with the Equal Error Rate respectively are 1.755%, 1.95%. In this paper, we describe our system developed for the booth two tasks in the VLSP2021 Speaker Verification shared-task.