  Most emotion recognition systems fail in real-life situations (in the wild
scenarios) where the audio is contaminated by reverberation. Our study explores
new methods to alleviate the performance degradation of Speech Emotion
Recognition (SER) algorithms and develop a more robust system for adverse
conditions. We propose processing multi-microphone signals to address these
challenges and improve emotion classification accuracy. We adopt a
state-of-the-art transformer model, the Hierarchical Token-semantic Audio
Transformer (HTS-AT), to handle multi-channel audio inputs. We evaluate two
strategies: averaging mel-spectrograms across channels and summing
patch-embedded representations. Our multimicrophone model achieves superior
performance compared to single-channel baselines when tested on real-world
reverberant environments.
