Speech prosody as a biosignal for physical pain detection

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

Research output: Contribution to journalConference articlepeer-review

20 Scopus citations


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.

Original languageEnglish
Pages (from-to)420-424
Number of pages5
JournalProceedings of the International Conference on Speech Prosody
StatePublished - 2016
Externally publishedYes
Event8th Speech Prosody 2016 - Boston, United States
Duration: 31 May 20163 Jun 2016


  • Machine learning
  • Signal processing
  • Speech prosody in pain
  • Statistical classifiers


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