Predicting Heart Activity from Speech using Data-driven and Knowledge-based features
<|reference_start|>Predicting Heart Activity from Speech using Data-driven and Knowledge-based features: 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.<|reference_end|>
@article{elbanna2024predicting,
    title={Predicting Heart Activity from Speech using Data-driven and
  Knowledge-based features},
    author={Gasser Elbanna, Zohreh Mostaani and Mathew Magimai.-Doss},
    journal={arXiv preprint arXiv:2406.06341},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06341},
    primaryClass={cs.SD cs.AI eess.AS eess.SP}
}