Most recordings of verbal fluency tasks include substantial amounts of task-irrelevant content that could provide clinically valuable information for the detection of mild cognitive impairment (MCI). We developed a method for the analysis of verbal fluency, focusing not on the task-relevant words but on the silent segments, the hesitations, and the irrelevant utterances found in the voice recordings.

Phonemic (‘k’, ‘t’, ‘a’) and semantic (animals, food items, actions) verbal fluency data were collected from healthy control (HC; n = 25; M age = 67.32) and MCI (n = 25; M age = 71.72) participants. After manual annotation of the voice samples, 10 temporal parameters were computed based on the silent and the task-irrelevant segments. Traditional fluency measures, based on word count (correct words, errors, repetitions) were also employed in order to compare the outcome of the two methods.

Two silence-based parameters (the number of silent pauses and the average length of silent pauses) and the average word transition time differed significantly between the two groups in the case of all three semantic fluency tasks. Subsequent receiver operating characteristic (ROC) analysis showed that these three temporal parameters had classification abilities similar to the traditional measure of counting correct words.

In our approach for verbal fluency analysis, silence-related parameters displayed classification ability similar to the most widely used traditional fluency measure. Based on these results, an automated tool using voiced-unvoiced segmentation may be developed enabling swift and cost-effective verbal fluency-based MCI screening.