  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.
