Active authentication refers to a new mode of identity verification in which biometric indicators are continuously tested to provide real-time or near real-time monitoring of an authorized access to a service or use of a device. This is in contrast to the conventional authentication systems where a single test in form of a verification token such as a password is performed. In active voice authentication (AVA), voice is the biometric modality. This paper describes an ensemble of techniques that make reliable speaker verification possible using unconventionally short voice test signals. These techniques include model adaptation and minimum verification error (MVE) training that are tailored for the extremely short training and testing requirements. A database of 25 speakers is recorded for developing this system. In our off-line evaluation on this dataset, the system achieves an average windowed-based equal error rates of 3-4% depending on the model configuration, which is remarkable considering that only 1 second of voice data is used to make every single authentication decision. On the NIST SRE 2001 Dataset, the system provides a 3.88% absolute gain over i-vector when the duration of test segment is 1 second. A real-time demonstration system has been implemented on Microsoft Surface Pro.

主动身份验证是指一种新的身份验证模式，在该模式中，生物识别指标持续不断经过测试以提供对服务或设备使用的授权访问的实时或近实时监视。这与常规认证系统相反，在常规认证系统中，执行诸如密码之类的验证令牌形式的单个测试。在主动语音身份验证（AVA）中，语音是生物特征形式。本文介绍了使用非常规的短语音测试信号使可靠的扬声器验证成为可能的一系列技术。这些技术包括模型调整和最小验证误差（MVE）培训，这些培训是针对极短的培训和测试要求量身定制的。记录了25位发言人的数据库，用于开发此系统。在我们对该数据集的离线评估中，根据模型配置，该系统可实现平均3-4％的基于窗口的均等错误率，考虑到仅使用1秒钟的语音数据来做出每一个身份验证决策，这一点就非常明显。在NIST SRE 2001数据集上，当测试段的持续时间为1秒时，该系统比i矢量提供3.88％的绝对增益。在Microsoft Surface Pro上已实现了实时演示系统。