AbstractBackgroundConnected speech is sensitive on detecting cognitive decline in early Alzheimer disease. In this study, we apply artificial intelligence to develop an automatic assessment for analyzing speech data.Method79 samples of picture description speech data were collected. All participants were native Chinese speakers including 31 normal controls (59‐88 yrs) and 49 patients (amnestic mild cognitive impairment and early Alzheimer’s disease) (58‐88 yrs). We extracted 16 linguistic features from the manually transcribed text. These linguistic features were generated by well‐trained linguists or automatically processed by the machine. We also trained decision tree models with those two sets of linguistic features. Seventy percent of the data was used for training and the remainder was used for testing. We also applied a cross‐validation method to avoid overfitting.ResultTotal words, unique words, and content words generated from linguists or machines were highly correlated (about r = 0.99, p < .001). However, the correlations between verb ratio, mean length of sentences, and filler ratio were relatively low (about r = 0.75, p < .001). With manually corrected features, the model could achieve 91.67% accuracy. Among them, the pronoun ratio and MLU (mean length of utterance) appeared to be equally crucial for model prediction. By contrast, with automatically generated features, the model achieved 83.33% accuracy only. MLU and MLS (mean length of sentences) were essential features for prediction.ConclusionAutomatic assessment could also achieve a good accuracy rate compared to humans (83.33% v.s. 91.67%). Some linguistic features such as fillers or sentences were still highly dependent on the expert’s judgment. Interestingly, MLU was critical in the prediction model of automatic or experts.
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