  In the field of emotion recognition and Human-Machine Interaction (HMI),
personalised approaches have exhibited their efficacy in capturing
individual-specific characteristics and enhancing affective prediction
accuracy. However, personalisation techniques often face the challenge of
limited data for target individuals. This paper presents our work on an
enhanced personalisation strategy, that leverages data augmentation to develop
tailored models for continuous valence and arousal prediction. Our proposed
approach, Distance Weighting Augmentation (DWA), employs a weighting-based
augmentation method that expands a target individual's dataset, leveraging
distance metrics to identify similar samples at the segment-level. Experimental
results on the MuSe-Personalisation 2023 Challenge dataset demonstrate that our
method significantly improves the performance of features sets which have low
baseline performance, on the test set. This improvement in poor-performing
features comes without sacrificing performance on high-performing features. In
particular, our method achieves a maximum combined testing CCC of 0.78,
compared to the reported baseline score of 0.76 (reproduced at 0.72). It also
achieved a peak arousal and valence scores of 0.81 and 0.76, compared to
reproduced baseline scores of 0.76 and 0.67 respectively. Through this work, we
make significant contributions to the advancement of personalised affective
computing models, enhancing the practicality and adaptability of data-level
personalisation in real world contexts.
