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 knownfeatures. 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.
|Number of pages||5|
|Journal||Proceedings of the International Conference on Speech Prosody|
|State||Published - 2016|
|Event||8th Speech Prosody 2016 - Boston, United States|
Duration: 31 May 2016 → 3 Jun 2016
Bibliographical notePublisher Copyright:
© 2016, International Speech Communications Association. All rights reserved.
- Machine learning
- Signal processing
- Speech prosody in pain
- Statistical classifiers