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Individuals with impaired hearing experience difficulty in conversations,especially in noisy environments. This difficulty often manifests as a changein behavior and may be captured via facial expressions, such as the expressionof discomfort or fatigue. In this work, we build on this idea and introduce theproblem of detecting hearing loss from an individual's facial expressionsduring a conversation. Building machine learning models that can representhearing-related facial expression changes is a challenge. In addition, modelsneed to disentangle spurious age-related correlations from hearing-drivenexpressions. To this end, we propose a self-supervised pre-training strategytailored for the modeling of expression variations. We also use adversarialrepresentation learning to mitigate the age bias. We evaluate our approach on alarge-scale egocentric dataset with real-world conversational scenariosinvolving subjects with hearing loss and show that our method for hearing lossdetection achieves superior performance over baselines.

Hearing Loss Detection from Facial Expressions in One-on-one Conversations