Development and validation of a self-administered computerized cognitive assessment based on automatic speech recognition

by Hyun-Ho Kong, Kwangsoo Shin, Dong-Seok Yang, Aryun Kim, Hyeon-Seong Joo, Min Woo Oh, Jeonghwan Lee

Existing computerized cognitive tests (CCTs) lack speech recognition, which limits their assessment of language function. Therefore, we developed CogMo, a self-administered CCT that uses automatic speech recognition (ASR) to assess multi-domain cognitive functions, including language. This study investigated the validity and reliability of CogMo in discriminating cognitive impairments. CogMo automatically provides CCT results; however, manual scoring using recorded audio was performed to verify its ASR accuracy. The mini–mental state examination (MMSE) was used to assess cognitive functions. Pearson’s correlation was used to analyze the relationship between the MMSE and CogMo results, intraclass correlation coefficient (ICC) was used to evaluate the test-retest reliability of CogMo, and receiver operating characteristic (ROC) analysis validated its diagnostic accuracy for cognitive impairments. Data of 100 participants (70 with normal cognition, 30 with cognitive impairment), mean age 74.6±7.4 years, were analyzed. The CogMo scores indicated significant differences in cognitive levels for all test items, including manual and automatic scoring for the speech recognition test, and a very high correlation (r = 0.98) between the manual and automatic CogMo scores. Additionally, the total CogMo and MMSE scores exhibited a strong correlation (r = 0.89). Moreover, CogMo exhibited high test-retest reliability (ICC = 0.94) and ROC analysis yielded an area under the curve of 0.89 (sensitivity = 90.0%, specificity = 82.9%) at a cutoff value of 68.8 points. The CogMo demonstrated adequate validity and reliability for discriminating multi-domain cognitive impairment, including language function, in community-dwelling older adults.