Predicting acute pain levels implicitly from vocal features

Abstract

Evaluating pain in speech represents a critical challenge in high-
stakes clinical scenarios, from analgesia delivery to emergency
triage. Clinicians have predominantly relied on direct verbal
communication of pain which is difficult for patients with com-
munication barriers, such as those affected by stroke, autism,
and learning difficulties. Many previous efforts have focused
on multimodal data which does not suit all clinical applications.
Our work is the first to collect a new English speech dataset
wherein we have induced acute pain in adults using a cold pres-
sor task protocol and recorded subjects reading sentences out
loud. We report pain discrimination performance as F1 scores
from binary (pain vs. no pain) and three-class (mild, moder-
ate, severe) prediction tasks, and support our results with ex-
plainable feature analysis. Our work is a step towards provid-
ing medical decision support for pain evaluation from speech to
improve care across diverse and remote healthcare settings

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Identifiers

ORCID for Jennifer Williams: orcid.org/0000-0003-1410-0427

ORCID for Eike Schneiders: orcid.org/0000-0002-8372-1684

ORCID for Tina Seabrooke: orcid.org/0000-0002-4119-8389

ORCID for Ganesh Vigneswaran: orcid.org/0000-0002-4115-428X

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