  This work describes the speaker verification system developed by Human
Language Technology Laboratory, National University of Singapore (HLT-NUS) for
2019 NIST Multimedia Speaker Recognition Evaluation (SRE). The multimedia
research has gained attention to a wide range of applications and speaker
recognition is no exception to it. In contrast to the previous NIST SREs, the
latest edition focuses on a multimedia track to recognize speakers with both
audio and visual information. We developed separate systems for audio and
visual inputs followed by a score level fusion of the systems from the two
modalities to collectively use their information. The audio systems are based
on x-vector based speaker embedding, whereas the face recognition systems are
based on ResNet and InsightFace based face embeddings. With post evaluation
studies and refinements, we obtain an equal error rate (EER) of 0.88% and an
actual detection cost function (actDCF) of 0.026 on the evaluation set of 2019
NIST multimedia SRE corpus.
