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Foundation Models trained to perform a certain task can be fine-tuned to other tasks with limited data and computational resources. The advantage of such practice is that it makes it possible to benefit, at least indirectly, from the large amounts of data and the major computational infrastructure necessary for training a Foundation Model. However, there is a limitation too, namely that the few organizations that have the major resources necessary to develop and train Foundation Models do it only for the modalities that are of interest to them. For this reason, this article proposes to fine-tune Foundation Models trained on speech and photoplethysmography signals to perform stress detection based on Electro-Dermal Activity, a modality for which no Foundation Model exists. To the best of our knowledge, this is one of the first works proposing experiments of this type and the results show state-of-the-art stress detection performances over a publicly available benchmark, even if speech and photoplethysmograhpy data differ significantly from Electro-Dermal Activity signals.
Aldossary et al. (Sat,) studied this question.