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.精准的疼痛评估对于医疗人员开展恰当的治疗至关重要。然而，受言语障碍、语言壁垒等多种因素影响，并非所有患者都能清晰表述自身疼痛。在此类场景下，医护人员只能依靠非言语体征来判断患者的疼痛程度。TAME疼痛（TAME Pain）项目旨在通过先进的音频分析技术，为可靠疼痛评估工具的研发奠定基础。 本项目旨在构建一套涵盖声学信号的多维度数据集，以实现疼痛程度的精准预测。该数据集已获得德克萨斯大学奥斯汀分校机构审查委员会（IRB编号：STUDY00004954）的伦理批准，可用于探究从承受疼痛刺激的健康受试者身上提取的声学与非声学信号能否有效反映疼痛程度。我们通过对数据集内每一条音频文件——包括受试者说出的每一句话——进行详细标注来丰富该数据集，标注内容涵盖背景噪声、前景噪声、语音错误以及非语音发声特征等信息。此类标注不仅可支撑全面的音频分析、助力疼痛相关研究，还能帮助挖掘语音与非语音层面的疼痛表征线索。本数据集可为疼痛评估技术领域的研究人员与开发者提供宝贵的研究资源。 本数据集的采集与构建工作以德克萨斯大学奥斯汀分校为核心依托，并获得了英国诺丁汉大学与南安普敦大学的协作支持。

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。