Early Dementia Detection Using Multiple Spontaneous Speech Prompts: The PROCESS Challenge

Dementia is associated with various cognitive impairments and typically manifests only after significant progression, making intervention at this stage often ineffective. To address this issue, the Prediction and Recognition of Cognitive Decline through Spontaneous Speech (PROCESS) Signal Processing Grand Challenge invites participants to focus on early-stage dementia detection. We provide a new spontaneous speech corpus for this challenge. This corpus includes answers from three prompts designed by neurologists to better capture the cognition of speakers. Our baseline models achieved an F1-score of 55.0% on the classification task and an RMSE of 2.98 on the regression task.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10889017
Citations
12
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
25.42

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article

Early Dementia Detection Using Multiple Spontaneous Speech Prompts: The PROCESS Challenge

Madhurananda Pahar, Ronan C. O’Malley, Daniel Braun, Fasih Haider et al.
12 citations
Emotion and Mood Recognition
25.42
article

Early Dementia Detection Using Multiple Spontaneous Speech Prompts: The PROCESS Challenge

Madhurananda Pahar, Ronan C. O’Malley, Daniel Braun, Fasih Haider, Heidi Christensen, D. Blackburn, Saturnino Luz, Simon Bell, F. E. Peters, Fuxiang Tao, Hend ElGhazaly, Bahman Mirheidari, Xiao Yao, S. Young, Caitlin Illingworth
article en
12 citations

Abstract

Dementia is associated with various cognitive impairments and typically manifests only after significant progression, making intervention at this stage often ineffective. To address this issue, the Prediction and Recognition of Cognitive Decline through Spontaneous Speech (PROCESS) Signal Processing Grand Challenge invites participants to focus on early-stage dementia detection. We provide a new spontaneous speech corpus for this challenge. This corpus includes answers from three prompts designed by neurologists to better capture the cognition of speakers. Our baseline models achieved an F1-score of 55.0% on the classification task and an RMSE of 2.98 on the regression task.

University of Sheffield (GB), University of Edinburgh (GB)
HORIZON EUROPE Framework Programme
Reduced inequalities
Openalex Percentile: Top 0%
Emotion and Mood Recognition
25.42
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