  Communicating in noisy, multi-talker environments is challenging, especially
for people with hearing impairments. Egocentric video data can potentially be
used to identify a user's conversation partners, which could be used to inform
selective acoustic amplification of relevant speakers. Recent introduction of
datasets and tasks in computer vision enable progress towards analyzing social
interactions from an egocentric perspective. Building on this, we focus on the
task of identifying conversation partners from egocentric video and describe a
suitable dataset. Our dataset comprises 69 hours of egocentric video of diverse
multi-conversation scenarios where each individual was assigned one or more
conversation partners, providing the labels for our computer vision task. This
dataset enables the development and assessment of algorithms for identifying
conversation partners and evaluating related approaches. Here, we describe the
dataset alongside initial baseline results of this ongoing work, aiming to
contribute to the exciting advancements in egocentric video analysis for social
settings.
