Research Centre for Linguistics, Eötvös Loránd Research Network, Budapest, Hungary.
Eötvös Lorand Research Network - University of Szeged, Research Group on Artificial Intelligence, Szeged, Hungary.
Clin Linguist Phon. 2023 Jun 3;37(4-6):549-566. doi: 10.1080/02699206.2023.2170830. Epub 2023 Jan 30.
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.
我们的研究对象是复发缓解型多发性硬化症(RRMS)。在一半的 RRMS 病例中,存在轻度认知障碍,但尽管它们对个体的日常生活有不利影响,但往往仍未被发现。使用语音分析来检测微妙的认知改变在 MS 研究中很少实施。我们应用自动语音识别技术来设计具有潜在诊断价值的语音任务。因此,我们使用了两个针对 RRMS 的神经和认知特征进行调整的叙事任务;即叙事回忆和个人叙事。除了语音分析,我们还检查了信息处理速度、工作记忆、言语流畅性和命名技能。21 名 RRMS 患者和 21 名性别、年龄和教育程度匹配的健康对照者参加了这项研究。所有 RRMS 患者在 Addenbrooke 认知测验中均表现正常。测量了以下语音参数:发音和语速、无声和填充停顿的比例、持续时间、频率和平均长度。我们发现组间和语音任务之间存在显著的时间参数差异。ROC 分析对叙事回忆任务产生了较高的分类准确率(0.877 和 0.866),但对个人叙事任务的准确率较低(0.617 和 0.592)。信息处理速度影响 RRMS 组的语音,但不影响对照组的语音。叙事回忆任务的认知负荷较高可能是 RRMS 组相对于对照组语音发生显著变化的原因。结果表明,叙事回忆任务可能是检测 RRMS 中微妙认知变化的有效方法。