Accurately predicting heart activity and other biological signals is crucial for diagnosis and monitoring. Given that speech is an outcome of multiple physiological systems, a significant body of work studied the acoustic correlates of heart activity. Recently, self-supervised models have excelled in speech-related tasks compared to traditional acoustic methods. However, the robustness of data-driven representations in predicting heart activity remained unexplored. In this study, we demonstrate that self-supervised speech models outperform acoustic features in predicting heart activity parameters. We also emphasize the impact of individual variability on model generalizability. These findings underscore the value of data-driven representations in such tasks and the need for more speech-based physiological data to mitigate speaker-related challenges.
@article{arxiv.2406.06341,
title = {Predicting Heart Activity from Speech using Data-driven and Knowledge-based features},
author = {Gasser Elbanna and Zohreh Mostaani and Mathew Magimai. -Doss},
journal= {arXiv preprint arXiv:2406.06341},
year = {2024}
}