Affiliations: [a] University of Cambridge, Language Technology Lab, Cambridge, UK
| [b] Winterlight Labs, Toronto, ON, Canada
Correspondence:
[*]
Correspondence to: Ulla Petti, University of Cambridge, Language Technology Lab, 9 West Road, Cambridge CB3 9DP, UK. Tel.: +44 07751097441; E-mail: ump20@cam.ac.uk.
Abstract: Background:Language impairment in Alzheimer’s disease (AD) has been widely studied but due to limited data availability, relatively few studies have focused on the longitudinal change in language in the individuals who later develop AD. Significant differences in speech have previously been found by comparing the press conference transcripts of President Bush and President Reagan, who was later diagnosed with AD. Objective:In the current study, we explored whether the patterns previously established in the single AD-healthy control (HC) participant pair apply to a larger group of individuals who later receive AD diagnosis. Methods:We replicated previous methods on two larger corpora of longitudinal spontaneous speech samples of public figures, consisting of 10 and 9 AD-HC participant pairs. As we failed to find generalizable patterns of language change using previous methodology, we proposed alternative methods for data analysis, investigating the benefits of using different language features and their change with age, and compiling the single features into aggregate scores. Results:The single features that showed the strongest results were moving average type:token ratio (MATTR) and pronoun-related features. The aggregate scores performed better than the single features, with lexical diversity capturing a similar change in two-thirds of the participants. Conclusion:Capturing universal patterns of language change prior to AD can be challenging, but the decline in lexical diversity and changes in MATTR and pronoun-related features act as promising measures that reflect the cognitive changes in many participants.
Keywords: Alzheimer’s disease, biomarker, cognitive dysfunction, dementia, early diagnosis, language, medical informatics, natural language processing, speech
