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

Tao, Fuxiang, Mirheidari, Bahman, Pahar, Madhurananda, Young, Sophie, Xiao, Yao, Elghazaly, Hend, Peters, Fritz, Illingworth, Caitlin, Braun, Dorota, O'Malley, Ronan, Bell, Simon, Blackburn, Daniel, Haider, Fasih, Luz, Saturnino, Christensen, Heidi

arXiv.org Artificial Intelligence 

Second, the audio quality of the data is poor and does not represent the quality that it is possible to Dementia is associated with various cognitive impairments achieve even with current, standard consumer-based devices and typically manifests only after significant progression, like modern laptops. These factors underscore the necessity making intervention at this stage often ineffective. To address for new data sets to ensure the continued advancement and this issue, the Prediction and Recognition of Cognitive accuracy of research in this field. Decline through Spontaneous Speech (PROCESS) Signal The PROCESS Signal Processing Grand Challenge aims Processing Grand Challenge invites participants to focus on to establish a platform for contributions and discussions on early-stage dementia detection. We provide a new spontaneous early-stage dementia detection using speech signal processing speech corpus for this challenge. This corpus includes and Artificial Intelligence (AI) models. To support this, answers from three prompts designed by neurologists to better we provide a state-of-the-art corpus covering a broader range capture the cognition of speakers. Our baseline models of diagnostic classes for different subtypes of early-stage achieved an F1-score of 55.0% on the classification task and dementia, including mild cognitive impairment (MCI).