Speech prosody as a biosignal for physical pain detection

Yaniv Oshrat, Ayala Bloch, Anat Lerner, Azaria Cohen, Mireille Avigal, Gabi Zeilig

Obtaining an objective assessment of pain is an important challenge for clinicians. The purpose of this study is to examine the connections between subjective reports of pain and measureable biosignals of human speech prosody, as a step towards coping with this challenge. Patients reporting pain were voice-recorded to attain reports on different levels of pain. Recording was done in the patients' natural environment at the medical center. Features were extracted from the voice-recordings, including features that were exclusively developed for this study. A machine-learning based classification process was performed in order to distinguish between samples with "no significant pain" and with "significant pain" reported. This classification process distinguished well between the two categories. Moreover, features developed during this study improved classification results in comparison to classification based solely on known-features. Results indicate that there is evidence of a connection between measureable biosignal parameters of speech and the simultaneous self-reported pain level. This finding might be useful for developing future methods to more objective assessment of pain.

DOI: 10.21437/SpeechProsody.2016-86

Cite as

Oshrat, Y., Bloch, A., Lerner, A., Cohen, A., Avigal, M., Zeilig, G. (2016) Speech prosody as a biosignal for physical pain detection. Proc. Speech Prosody 2016, 420-424.

author={Yaniv Oshrat and Ayala Bloch and Anat Lerner and Azaria Cohen and Mireille Avigal and Gabi Zeilig},
title={Speech prosody as a biosignal for physical pain detection},
booktitle={Speech Prosody 2016},