Spoken Language Derived Measures for Detecting Mild Cognitive Impairment

Abstract

Spoken responses produced by subjects during neuropsychological exams can provide diagnostic markers beyond exam performance. In particular, characteristics of the spoken language itself can discriminate between subject groups. We present results on the utility of such markers in discriminating between healthy elderly subjects and subjects with mild cognitive impairment (MCI). Given the audio and transcript of a spoken narrative recall task, a range of markers are automatically derived. These markers include speech features such as pause frequency and duration, and many linguistic complexity measures. We examine measures calculated from manually annotated time alignments (of the transcript with the audio) and syntactic parse trees, as well as the same measures calculated from automatic (forced) time alignments and automatic parses. We show statistically significant differences between clinical subject groups for a number of measures. These differences are largely preserved with automation. We then present classification results, and demonstrate a statistically significant improvement in the area under the ROC curve (AUC) when using automatic spoken language derived features in addition to the neuropsychological test scores. Our results indicate that using multiple, complementary measures can aid in automatic detection of MCI.

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

“…These problems are present from the very early stages of the disease, and progress and worsen at the same time that the cognitive decline [276,283]. Due to the ease with which voice recordings can be obtained, in the recent years, speech features have been used in works related to automatic MCI and AD diagnosis systems [27,40,[284][285][286][287][288]. Nonetheless, few studies report speech based classification results that suggest that they have potential to become part of a multimodal diagnosis system.…”

“…Nonetheless, few studies report speech based classification results that suggest that they have potential to become part of a multimodal diagnosis system. Roark et al have demonstrated that automatic speech characteristics measuring techniques in combination of neuropsychological tests can help in improving MCI diagnosis [287,288]. Khodabakhsh et al [27] have recently analysed the possibility of a diagnosis, using speech features extracted from telephone calls.…”

Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks

“…Automated discourse analysis tools based on Natural Language Processing (NLP) resources and tools aiming at the diagnosis of language-impairing dementias via machine learning methods are already available for the English language (Fraser et al, 2015b;Yancheva et al, 2015;Roark et al, 2011) and also for Brazilian Portuguese (BP) (Aluísio et al, 2016). The latter study used a publicly available tool, Coh-Metrix-Dementia 1 , to extract 73 textual metrics of narrative transcripts, comprising several levels of linguistic analysis from word counts to semantics and discourse.…”

Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts

“…Although there are well known tests such as the Mini Mental Test, they are usually not sensitive enough to reliably filter out MCI in its early stage. Tests on linguistic memory prove more efficient in detecting MCI, but they tend to yield a relatively high number of false positive diagnoses (Roark et al, 2011).…”