  In this work, we explore a novel few-shot personalisation architecture for
emotional vocalisation prediction. The core contribution is an `enrolment'
encoder which utilises two unlabelled samples of the target speaker to adjust
the output of the emotion encoder; the adjustment is based on dot-product
attention, thus effectively functioning as a form of `soft' feature selection.
The emotion and enrolment encoders are based on two standard audio
architectures: CNN14 and CNN10. The two encoders are further guided to forget
or learn auxiliary emotion and/or speaker information. Our best approach
achieves a CCC of $.650$ on the ExVo Few-Shot dev set, a $2.5\%$ increase over
our baseline CNN14 CCC of $.634$.
