Predicting Heart Activity from Speech using Data-driven and Knowledge-based features

Autor:	Elbanna, Gasser, Mostaani, Zohreh, -Doss, Mathew Magimai.
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Audio and Speech Processing Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at Interspeech 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.06341 Zobrazit plný text záznamu View this record from Arxiv