3-002
Speech-Based Detection of Mild Cognitive Impairment using Acoustic Features
○Shiannfong Huang・Ching-Kai Chen・Jun-Yi Lin(Asia Eastern University of Science and Technology)
This study introduces a speech-based system for detecting mild cognitive impairment (MCI) by analyzing specific acoustic features. With the growing need for early diagnosis of MCI, which often precedes more severe neurodegenerative diseases, this system applies speech signal processing to capture linguistic markers associated with cognitive decline. Our findings indicate that acoustic features, including pauses, repetition rates, and pitch variations, provide reliable indicators of MCI, supporting the potential of non-invasive, accessible screening tools.