  Individuals with impaired hearing experience difficulty in conversations,
especially in noisy environments. This difficulty often manifests as a change
in behavior and may be captured via facial expressions, such as the expression
of discomfort or fatigue. In this work, we build on this idea and introduce the
problem of detecting hearing loss from an individual's facial expressions
during a conversation. Building machine learning models that can represent
hearing-related facial expression changes is a challenge. In addition, models
need to disentangle spurious age-related correlations from hearing-driven
expressions. To this end, we propose a self-supervised pre-training strategy
tailored for the modeling of expression variations. We also use adversarial
representation learning to mitigate the age bias. We evaluate our approach on a
large-scale egocentric dataset with real-world conversational scenarios
involving subjects with hearing loss and show that our method for hearing loss
detection achieves superior performance over baselines.
