TAME Pain: Trustworthy AssessMEnt of Pain from Speech and Audio for the Empowerment of Patients

Precise pain assessment is essential for medical professionals to provide
appropriate treatment. However, not every patient can verbalize their pain due
to various reasons, such as speech disorders or language barriers. In these
cases, medical practitioners must rely on non-verbal signs to determine the
pain level. The TAME Pain project aims to pave the way for the development of
reliable pain assessment tools through advanced audio analysis.

We aim to create a comprehensive dataset that captures acoustic signals to
accurately predict pain levels. This dataset, approved by the University of
Texas at Austin's institutional review board (IRB number: STUDY00004954), will
enable the investigation of whether acoustic and non-acoustic signals
extracted from healthy individuals subjected to pain can reliably indicate
pain levels. We augment this dataset by annotating every single audio file,
including every sentence spoken by the participant, with details such as
background and foreground noise, speech errors, and non-speech vocal features.
These annotations enable thorough audio analysis, facilitate pain studies, and
aid in identifying both speech and non-speech pain cues. This dataset provides
a resource for researchers and developers working on pain assessment
technologies.

The data collection and creation efforts are based at the University of Texas
at Austin, with collaborative input from the University of Nottingham and the
University of Southampton in the UK.

搜集汇总

背景与挑战

背景概述

TAME Pain数据集是一个专注于通过语音和音频进行疼痛评估的研究资源，包含51名健康参与者在冷加压任务诱导疼痛下采集的7,039个标注语音样本，总音频时长约311分钟。每个样本均带有自报告疼痛等级（1-10分），并附有详细的元数据和噪声标注，旨在支持开发可靠的、基于AI的疼痛评估工具，尤其适用于远程医疗和非言语人群的应用场景。