Abstract: This paper describes ODESSA and PLUMCOT submissions to Albayzin Multimodal Diarization Challenge 2018. Given a list of people to recognize (alongside image and short video samples of those people), the task consists in jointly answering the two questions "who speaks when?" and "who appears when?". Both consortia submitted 3 runs (1 primary and 2 contrastive) based on the same underlying monomodal neural technologies: neural speaker segmentation, neural speaker embeddings, neural face embeddings, and neural talking-face detection. Our submissions aim at showing that face clustering and recognition can (hopefully) help to improve speaker diarization.

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