SV - VLSP2021: Smartcall - ITS的系统

Hung Van Dinh, Tuan Van Mai, Quyen B. Dam, Bao Quoc Nguyen
{"title":"SV - VLSP2021: Smartcall - ITS的系统","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":"{\"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}","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

摘要

本文介绍了智能呼叫ITS系统在越南语语言语音处理、说话人验证(SV)任务中的应用。挑战包括两项任务,重点是在有限数据下开发SV模型和测试SV系统的鲁棒性。在这两个任务中,我们使用了不同架构的各种预训练的说话人嵌入模型:TDNN, Resnet34。在使用组织者的数据进行特定的微调策略后,我们的系统在两个任务上都获得了第一名,错误率分别为1.755%和1.95%。在本文中,我们描述了我们在VLSP2021扬声器验证共享任务中为展台两个任务开发的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SV - VLSP2021: The Smartcall - ITS’s Systems
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信