论文标题
部分可观测时空混沌系统的无模型预测
Baselines and Protocols for Household Speaker Recognition
论文作者
论文摘要
扬声器对家用设备（例如智能扬声器）的认可具有一些挑战：（i）在许多异质域（家庭），（ii）短语，（iii）招募数据（被动招募）和（iv）存在不知名的人（宾客）的稳健性（iii）可能没有扬声器标签。尽管存在许多商业产品，但发表的研究较少，没有公共可用的评估协议或开源基准。我们的工作通过提供源自公共资源（Voxceleb和Asvspoof 2019数据）的可访问评估基准以及初步的开源基线来弥补这一差距。这包括四种用于主动注册的算法（可用的扬声器标签）和一种用于被动注册的算法。
Speaker recognition on household devices, such as smart speakers, features several challenges: (i) robustness across a vast number of heterogeneous domains (households), (ii) short utterances, (iii) possibly absent speaker labels of the enrollment data (passive enrollment), and (iv) presence of unknown persons (guests). While many commercial products exist, there is less published research and no publicly-available evaluation protocols or open-source baselines. Our work serves to bridge this gap by providing an accessible evaluation benchmark derived from public resources (VoxCeleb and ASVspoof 2019 data) along with a preliminary pool of open-source baselines. This includes four algorithms for active enrollment (speaker labels available) and one algorithm for passive enrollment.
