Aided diagnosis of dementia type through computer-based analysis of spontaneous speech

Abstract

This pilot study evaluates the ability of machined learned algorithms to assist with the differential diagnosis of dementia subtypes based on brief (< 10 min) spontaneous speech samples. We analyzed 1 recordings of a brief spontaneous speech sample from 48 participants from 5 different groups: 4 types of dementia plus healthy controls. Recordings were analyzed using a speech recognition system optimized for speakerindependent spontaneous speech. Lexical and acoustic features were automatically extracted. The resulting feature profiles were used as input to a machine learning system that was trained to identify the diagnosis assigned to each research participant. Between groups lexical and acoustic differences features were detected in accordance with expectations from prior research literature suggesting that classifications were based on features consistent with human-observed symptomatology. Machine learning algorithms were able to identify participants' diagnostic group with accuracy comparable to existing diagnostic methods in use today. Results suggest this clinical speech analytic approach offers promise as an additional, objective and easily obtained source of diagnostic information for clinicians.

Sentence segmentation of aphasic speech

“…One issue that arises with ASR is the introduction of word recognition errors: insertions, deletions, and substitutions. This problem as it relates to impaired speech has been considered elsewhere (Jarrold et al, 2014;Fraser et al, 2013;Rudzicz et al, 2014), although more work is needed. Another issue, which we address here, is how ASR transcripts are divided into sentences.…”

Detecting Dementia through Retrospective Analysis of Routine Blog Posts by Bloggers with Dementia

“…Recently, there have been attempts to combine clinical information with language analysis using machine learning and NLP techniques to aid in diagnosis of dementia, and to distinguish between types of pathologies (Jarrold et al, 2014;Rentoumi et al, 2014;Orimaye et al, 2014;Fraser et al, 2015;Masrani et al, 2017). This would provide an inexpensive, non-invasive and efficient screening tool to assist in early detection, treatment and institution of supports.…”

Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts

“…For instance, automatic speech recognition tools were employed in detecting aphasia (Fraser et al, 2013b;Fraser et al, 2014;Fraser et al, 2013a) and mild cognitive impairment (Lehr et al, 2012), and Alzheimer's Disease (Baldas et al, 2010;Satt et al, 2014). Jarrold et al (2014) distinguished four types of dementia on the basis of spontaneous speech samples. Lexical analysis of spontaneous speech may also indicate different types of dementia (Bucks et al, 2000;Holmes and Singh, 1996) and may be exploited in the automatic detection of patients suffering from dementia (Thomas et al, 2005).…”