Characterising spoken responses to an intelligent virtual agent by persons with mild cognitive impairment.

Phonetics dementia mild cognitive impairment prosody speech production measurement

Journal

Clinical linguistics & phonetics
ISSN: 1464-5076
Titre abrégé: Clin Linguist Phon
Pays: England
ID NLM: 8802622

Informations de publication

Date de publication:
04 03 2021
Historique:
pubmed: 20 6 2020
medline: 26 10 2021
entrez: 20 6 2020
Statut: ppublish

Résumé

The diagnosis of Mild Cognitive Impairment (MCI) characterises patients at risk of dementia and may provide an opportunity for disease-modifying interventions. Identifying persons with MCI (PwMCI) from adults of a similar age without cognitive complaints is a significant challenge. The main aims of this study were to determine whether generic speech differences were evident between PwMCI and healthy controls (HC), whether such differences were identifiable in responses to recent or remote memory questions, and to determine which speech variables showed the clearest between-group differences. This study analysed recordings of 8 PwMCI (5 females, 3 males) and 14 HC of a similar age (8 females, 6 males). Participants were recorded interacting with an intelligent virtual agent: a computer-generated talking head on a computer screen which asks pre-recorded questions when prompted by the interviewee through pressing the next key on a computer keyboard. Responses to recent and remote memory questions were analysed. Mann-Whitney U tests were used to test for statistically significant differences between PwMCI and HC on each of 12 speech variables, relating to temporal characteristics, number of words produced and pitch. It was found that compared to HC, PwMCI produce speech for less time and in shorter chunks, they pause more often and for longer, take longer to begin speaking and produce fewer words in their answers. It was also found that the PwMCI and HC were more alike when responding to remote memory questions than when responding to recent memory questions. These findings show great promise and suggest that detailed speech analysis can make an important contribution to diagnostic and stratification systems in patients with memory complaints.

Identifiants

pubmed: 32552087
doi: 10.1080/02699206.2020.1777586
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

237-252

Subventions

Organisme : Medical Research Council
ID : MC_PC_17176
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom

Auteurs

Gareth Walker (G)

School of English, University of Sheffield , Sheffield, UK.

Lee-Anne Morris (LA)

Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield , Sheffield, UK.

Heidi Christensen (H)

Department of Computer Science, University of Sheffield , Sheffield, UK.

Bahman Mirheidari (B)

Department of Computer Science, University of Sheffield , Sheffield, UK.

Markus Reuber (M)

Academic Neurology Unit, Royal Hallamshire Hospital, University of Sheffield , Sheffield, UK.

Daniel J Blackburn (DJ)

Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield , Sheffield, UK.

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Classifications MeSH