Early Dementia Detection Using Multiple Spontaneous Speech Prompts: The PROCESS Challenge
This work addresses early detection of dementia for potential intervention, but it is incremental as it focuses on providing a new dataset and baseline models.
The paper tackles early dementia detection by introducing a new spontaneous speech corpus with three neurologist-designed prompts, achieving baseline results of 55.0% F1-score for classification and 2.98 RMSE for regression.
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.