  The significance of mental health classification is paramount in contemporary
society, where digital platforms serve as crucial sources for monitoring
individuals' well-being. However, existing social media mental health datasets
primarily consist of text-only samples, potentially limiting the efficacy of
models trained on such data. Recognising that humans utilise cross-modal
information to comprehend complex situations or issues, we present a novel
approach to address the limitations of current methodologies. In this work, we
introduce a Multimodal and Multi-Teacher Knowledge Distillation model for
Mental Health Classification, leveraging insights from cross-modal human
understanding. Unlike conventional approaches that often rely on simple
concatenation to integrate diverse features, our model addresses the challenge
of appropriately representing inputs of varying natures (e.g., texts and
sounds). To mitigate the computational complexity associated with integrating
all features into a single model, we employ a multimodal and multi-teacher
architecture. By distributing the learning process across multiple teachers,
each specialising in a particular feature extraction aspect, we enhance the
overall mental health classification performance. Through experimental
validation, we demonstrate the efficacy of our model in achieving improved
performance.
