RÉSUMÉ
Objective:To automatically and rapidly detect mild cognitive impairment (MCI) in an objective manner using natural language processing (NLP).Methods:A total of 215 participants (half female) aged 50 to 80 were recruited for the study′s normal cognition and MCI groups. Speech tasks and the mini mental state examination (MMSE-2) were used to collect audio data and quantify cognitive functioning. Altogether 162 acoustic features were extracted including the speaking speed, syllable number, syllable duration, number of pauses, duration of pauses, the standard deviation of formant frequency and sound pressure variation. They were compared between the two groups and genders. Multiple regression analysis was used to formulate a model predicting MCI. The sensitivity, specificity and accuracy of its predictions were used to evaluate its predictive power.Results:There were significant differences between the two groups in 50 acoustic features including their pronunciation rhythm and pronunciation accuracy. Univariate correlation analysis revealed that the pronunciation rhythm was significantly associated with cognitive functioning. The sensitivity, specificity and accuracy of the model were 0.54, 0.80 and 0.69 for males and 0.00, 0.86 and 0.63 for females.Conclusion:MCI greatly affects pronunciation rhythm. Acoustic analysis based on NLP can detect MCI rapidly and objectively.