Predicting Heart Activity from Speech using Data-driven and Knowledge-based features
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917689301139456 |
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| author | Elbanna, Gasser Mostaani, Zohreh -Doss, Mathew Magimai. |
| author_facet | Elbanna, Gasser Mostaani, Zohreh -Doss, Mathew Magimai. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06341 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting Heart Activity from Speech using Data-driven and Knowledge-based features Elbanna, Gasser Mostaani, Zohreh -Doss, Mathew Magimai. Sound Artificial Intelligence Audio and Speech Processing Signal Processing 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. |
| title | Predicting Heart Activity from Speech using Data-driven and Knowledge-based features |
| topic | Sound Artificial Intelligence Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2406.06341 |