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.
翻译:主动认证是指一种新的身份核查模式,在这种模式中,生物鉴别指标不断进行测试,以便对授权获取某种服务或使用某种设备的情况进行实时或近近实时监测。这与传统的认证系统形成对照,在常规认证系统中,以验证符号的形式进行单一测试,例如密码。在主动语音认证(AVA)中,声音是生物鉴别模式。本文描述了使用非常规短声测试信号进行可靠的语音验证的各种技术组合。这些技术包括为极短的培训和测试要求量身定制的模型适应和最小核查错误(MVE)培训。为开发这个系统记录了一个25个发言者的数据库。在这个数据集的离线评价中,根据模型配置,该系统实现了平均基于窗口的3-4 % 的错误率。考虑到只有1秒的语音数据可用于作出每项单一的认证决定,这一点非常显著。在NITSRE 2001数据集中,当测试段持续时间为1秒时,该系统为i-VCtor提供了3.88%的绝对收益。在Microsoft Pro地面上安装了一个实时演示系统。