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Exploring speaker enrolment for few-shot personalisation in emotional vocalisation prediction
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abstract
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 $.634$.
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Cited by 1 Pith paper
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Few-shot Personalization via In-Context Learning for Speech Emotion Recognition based on Speech-Language Model
Meta-training a speech-language model with in-context learning lets it recognize emotions for unseen speakers using just a few labeled utterances from that speaker, outperforming prior enrollment-based methods on a ne...
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