论文标题
自动评估视听验证系统。爱情提交给NIST SRE挑战2019
Automatic Quality Assessment for Audio-Visual Verification Systems. The LOVe submission to NIST SRE Challenge 2019
论文作者
论文摘要
分数的融合是由独立的单峰零件组成的多模式生物识别系统的基石。在这项工作中，我们专注于质量依赖的融合来进行扬声器验证。为此，我们提出了一个通用模型，可以对面部和扬声器方式进行自动质量评估培训。该模型估计了单峰系统产生的表示的质量，然后将其用于增强说话者和面部验证模块的得分水平融合。我们证明了这种质量依赖性融合在最近的NIST SRE19音频挑战数据集上带来的改进。
Fusion of scores is a cornerstone of multimodal biometric systems composed of independent unimodal parts. In this work, we focus on quality-dependent fusion for speaker-face verification. To this end, we propose a universal model which can be trained for automatic quality assessment of both face and speaker modalities. This model estimates the quality of representations produced by unimodal systems which are then used to enhance the score-level fusion of speaker and face verification modules. We demonstrate the improvements brought by this quality-dependent fusion on the recent NIST SRE19 Audio-Visual Challenge dataset.
