VLSP 2021 - SV挑战:嘈杂环境下的越南语说话人验证

Vi Thanh Dat, Phạm Việt Thành, Nguyen Thi Thu Trang
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引用次数: 4

摘要

VLSP 2021是在胡志明市越南国立大学信息技术大学(unit - vnu - hcm)举办的第八届年度国际研讨会。这是我们第一次组织演讲者验证共享任务,分为两个子任务SV-T1和SV-T2。SV- t1侧重于开发有限数据的SV模型,SV- t2侧重于测试SV系统的能力和鲁棒性。为了促进鲁棒模型的发展,我们收集、处理并发布了一个嘈杂环境下的说话人数据集,其中包含50小时的语音和1300多个说话人身份。共有39支队伍报名参加了这个共享任务,15支队伍收到了数据集,最后,7支队伍提交了最终的解决方案。最佳解决方案利用英语预训练模型,SV-T1和SV-T2的相等错误率分别为1.755%和1.950%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VLSP 2021 - SV challenge: Vietnamese Speaker Verification in Noisy Environments
The VLSP 2021 is the eighth annual international workshop whose campaign was organized at the University of Information Technology, Vietnam National University, Ho Chi Minh City (UIT-VNU-HCM). This was the first time we organized the Speaker Verification shared task with two subtasks SV-T1 and SV-T2. SV-T1 focuses on the development of SV models with limited data, and SV-T2 focuses on testing the capability and the robustness of SV systems. With the aim to boost the development of robust models, we collected, processed, and published a speaker dataset in noisy environments containing 50 hours of speech and more than 1,300 speaker identities. A total of 39 teams registered to participate in this shared task, 15 teams received the dataset, and finally, 7 teams submitted final solutions. The best solution leveraged English pre-trained models and achieved 1.755% and 1.950% Equal Error Rate for SV-T1 and SV-T2 respectively.
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