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

Enrolment-based personalisation for improving individual-level fairness  in speech emotion recognition

To be widely adopted, 3D facial avatars need to be animated easily,
realistically, and directly, from speech signals. While the best recent methods
generate 3D animations that are synchronized with the input audio, they largely
ignore the impact of emotions on facial expressions. Instead, their focus is on
modeling the correlations between speech and facial motion, resulting in
animations that are unemotional or do not match the input emotion. We observe
that there are two contributing factors resulting in facial animation - the
speech and the emotion. We exploit these insights in EMOTE (Expressive Model
Optimized for Talking with Emotion), which generates 3D talking head avatars
that maintain lip sync while enabling explicit control over the expression of
emotion. Due to the absence of high-quality aligned emotional 3D face datasets
with speech, EMOTE is trained from an emotional video dataset (i.e., MEAD). To
achieve this, we match speech-content between generated sequences and target
videos differently from emotion content. Specifically, we train EMOTE with
additional supervision in the form of a lip-reading objective to preserve the
speech-dependent content (spatially local and high temporal frequency), while
utilizing emotion supervision on a sequence-level (spatially global and low
frequency). Furthermore, we employ a content-emotion exchange mechanism in
order to supervise different emotion on the same audio, while maintaining the
lip motion synchronized with the speech. To employ deep perceptual losses
without getting undesirable artifacts, we devise a motion prior in form of a
temporal VAE. Extensive qualitative, quantitative, and perceptual evaluations
demonstrate that EMOTE produces state-of-the-art speech-driven facial
animations, with lip sync on par with the best methods while offering
additional, high-quality emotional control.

为了实现面部三维动画的情感表达和语音的同步，研究者开发了一种名为 EMOTE 的系统，它通过在空间局部和时间高频率上对语音内容进行口型识别来训练表情，同时维持与语音同步且具有完全的情感表达。