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.

通过距离加权增强的个性化策略，本文提出了一种针对连续情绪价值和激活度预测开发的个性化模型，实验结果表明在低基线性能特征集上改善了性能，而在高性能特征上保持一致，在现实世界环境中提高了数据层面个性化的实用性和适应性。