The expression of emotion is highly individualistic. However, contemporary speech emotion recognition (SER) systems typically rely on population-level models that adopt a `one-size-fits-all' approach for predicting emotion. Moreover, standard evaluation practices measure performance also on the population level, thus failing to characterise how models work across different speakers. In the present contribution, we present a new method for capitalising on individual differences to adapt an SER model to each new speaker using a minimal set of enrolment utterances. In addition, we present novel evaluation schemes for measuring fairness across different speakers. Our findings show that aggregated evaluation metrics may obfuscate fairness issues on the individual-level, which are uncovered by our evaluation, and that our proposed method can improve performance both in aggregated and disaggregated terms.

通过个体差异进行个性化调整，提出了一种适应每个新说话人的SER模型的新方法，并提出了用于衡量不同说话人公平性的新颖评估方案。研究结果表明，聚合评估指标可能掩盖个人层面的公平问题，而我们的评估能揭示出这些问题，并且我们提出的方法可以在聚合和分解层面上提高性能。

基于注册的个性化方法，提高语音情感识别中的个体公平性