ArticleScientific data2025
TAME Pain data release: using audio signals to characterize pain.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Voice-Based Pain Level Classification for Sensor-Assisted Intelligent Care.Sensors (Basel, Switzerland) · 2026Article
- Pain Level Classification from Speech Using GRU-Mixer Architecture with Log-Mel Spectrogram Features.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.