ABSTRACT
Our research studied relapsing-remitting multiple sclerosis (RRMS). In half of the RRMS cases, mild cognitive difficulties are present, but often remain undetected despite their adverse effects on individuals' daily life. Detecting subtle cognitive alterations using speech analysis have rarely been implemented in MS research. We applied automatic speech recognition technology to devise a speech task with potential diagnostic value. Therefore, we used two narrative tasks adjusted for the neural and cognitive characteristics of RRMS; namely narrative recall and personal narrative. In addition to speech analysis, we examined the information processing speed, working memory, verbal fluency, and naming skills. Twenty-one participants with RRMS and 21 gender-, age-, and education-matched healthy controls took part in the study. All the participants with RRMS achieved a normal performance on Addenbrooke's Cognitive Examination. The following parameters of speech were measured: articulation and speech rate, the proportion, duration, frequency, and average length of silent and filled pauses. We found significant differences in the temporal parameters between groups and speech tasks. ROC analysis produced high classification accuracy for the narrative recall task (0.877 and 0.866), but low accuracy for the personal narrative task (0.617 and 0.592). The information processing speed affected the speech of the RRMS group but not that of the control group. The higher cognitive load of the narrative recall task may be the cause of significant changes in the speech of the RRMS group relative to the controls. Results suggest that narrative recall task may be effective for detecting subtle cognitive changes in RRMS.
