Mirheidari, B., Blackburn, D. orcid.org/0000-0001-8886-1283, Walker, T. orcid.org/0000-0002-2583-7232 et al. (2 more authors) (2019) Dementia detection using automatic analysis of conversations. Computer Speech and Language, 53. pp. 65-79. ISSN 0885-2308
Abstract
Neurogenerative disorders, like dementia, can affect a person's speech, language and as a consequence, conversational interaction capabilities. A recent study, aimed at improving dementia detection accuracy, investigated the use of conversation analysis (CA) of interviews between patients and neurologists as a means to differentiate between patients with progressive neurodegenerative memory disorder (ND) and those with (non-progressive) functional memory disorders (FMD). However, doing manual CA is expensive and difficult to scale up for routine clinical use. In this paper, we present an automatic classification system using an intelligent virtual agent (IVA). In particular, using two parallel corpora of respectively neurologist- and IVA-led interactions, we show that using acoustic, lexical and CA-inspired features enable ND/FMD classification rates of 90.0% for the neurologist-patient conversations, and 90.9% for the IVA-patient conversations. Analysis of the differentiating potential of individual features show that some differences exist between the IVA and human-led conversations, for example in average turn length of patients.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © Crown Copyright 2018. Published by Elsevier Ltd. This is an author produced version of a paper subsequently published in Computer Speech and Language. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Dementia detection; Conversation analysis; Speech recognition and segmentation; Processing of pathological speech |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Human Communication Sciences (Sheffield) The University of Sheffield > Sheffield Teaching Hospitals |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 Sep 2018 09:36 |
Last Modified: | 02 Aug 2019 00:43 |
Published Version: | https://doi.org/10.1016/j.csl.2018.07.006 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.csl.2018.07.006 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:136339 |