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<p>Accurately assessing pain through speech remains a challenge in medical practice, with profound implications for patient care and patient health outcomes. The TAME Pain dataset addresses this challenge by providing a comprehensive dataset that captures the relationship between induced acute pain and speech in adults. Utilizing the Cold Pressor Task (CPT) method to induce pain, we recorded over 7,000 utterances from 51 participants, correlating their self-reported pain levels with vocal cues. This dataset stands as the largest of its kind to date and includes comprehensive annotations detailing background noise, speech errors, and non-speech vocal features, maximizing its utility for in-depth audio analysis. Our dataset is designed to support the development of reliable, non-invasive pain assessment technologies, particularly in telemedicine and remote healthcare settings. By releasing these data, we aim to facilitate interdisciplinary research in psychology, medical science, and AI, fostering innovations that can enhance pain management practices and improve patient outcomes across diverse clinical environments.</p>

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TAME Pain data release: using audio signals to characterize pain

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月10日

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  • Tu-Quyen Dao,
  • Eike Schneiders,
  • Jennifer Williams,
  • John Robert Bautista,
  • Tina Seabrooke,
  • Ganesh Vigneswaran,
  • Rishik Kolpekwar,
  • Ritwik Vashistha,
  • Arya Farahi

摘要

Accurately assessing pain through speech remains a challenge in medical practice, with profound implications for patient care and patient health outcomes. The TAME Pain dataset addresses this challenge by providing a comprehensive dataset that captures the relationship between induced acute pain and speech in adults. Utilizing the Cold Pressor Task (CPT) method to induce pain, we recorded over 7,000 utterances from 51 participants, correlating their self-reported pain levels with vocal cues. This dataset stands as the largest of its kind to date and includes comprehensive annotations detailing background noise, speech errors, and non-speech vocal features, maximizing its utility for in-depth audio analysis. Our dataset is designed to support the development of reliable, non-invasive pain assessment technologies, particularly in telemedicine and remote healthcare settings. By releasing these data, we aim to facilitate interdisciplinary research in psychology, medical science, and AI, fostering innovations that can enhance pain management practices and improve patient outcomes across diverse clinical environments.