A comparison of clinician assessment of speech versus automated speech analysis in mild cognitive impairment and Alzheimer's dementia: Neuropsychiatry and behavioral neurology/Assessment/Measurement of neuropsychiatric/Behavioral and psychological symptoms. (7th December 2020)

• Record Type: Journal Article
• Authors: Yeung, Anthony; Iaboni, Andrea; Rochon, Elizabeth; Lavoie, Monica; Santiago, Calvin; Yancheva, Maria; Novikova, Jekaterina; Kaufman, Liam D.; Mostafa, Fariya
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 6
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.044181
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Language impairment is an important marker of neurocognitive disorders. Despite this, there is no universal system of terminology used to describe speech impairment and large inter‐rater variability can exist between clinicians assessing speech. The role of automated speech analysis is emerging as a novel and potentially more objective method to assess speech in individuals with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). No studies have analyzed whether variables extracted through automated speech analysis can also be correlated to speech characteristics identified by a clinician. We sought to: (1) investigate whether clinician‐identified speech characteristics could be correlated with acoustic and linguistic variables identified through automated speech analysis, and (2) through automated speech analysis, identify novel acoustic and linguistic variables that may be associated with MCI or AD. Method: Using the DementiaBank speech corpus (Cookie Theft picture description task), audio recordings from patients with possible/probable AD (n=10), MCI (n=10), and controls (n=10) were rated by clinicians. Four characteristics of speech were rated by clinicians, including: word‐finding difficulty, coherence, perseveration, and speech errors. Clinicians were blinded to each other's scoring. Where scores differed, a consensus rating was established. The speech recordings were then transcribed, and linguistic and acoustic variables were extracted through automated speech analysis. The correlation between clinician‐identified speech characteristics and the acoustic and linguistic variables were then compared. Result: A significant correlation (p < 0.05) was found between clinician‐identified speech characteristics and variables extracted through automated analysis. Average word length was correlated with word‐finding difficulty (ρ = 0.74); changes in syntactic construction (use of past tense, third person singular verbs, and subordinate clauses) were correlated with coherence (ρ = 0.51) and errors in speech (ρ = 0.58); similarity between consecutive utterances was correlated to perseveration (ρ = 0.68). Conclusion: In this exploratory study, variables extracted through automated acoustic and linguistic analysis of MCI and AD speech were strongly correlated to clinician‐identified speech characteristics. Our results suggest further investigation for using an automated, data‐driven approach to define and monitor subjective clinical speech descriptors in neurocognitive disorders.