Mirheidari-etal2019

From emcawiki
Revision as of 05:51, 23 August 2018 by ElliottHoey (talk | contribs) (Created page with "{{BibEntry |BibType=ARTICLE |Author(s)=Bahman Mirheidari; Daniel Blackburn; Traci Walker; Markus Reuber; Heidi Christensen |Title=Dementia detection using automatic analysis o...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search
Mirheidari-etal2019
BibType ARTICLE
Key Mirheidari-etal2018
Author(s) Bahman Mirheidari, Daniel Blackburn, Traci Walker, Markus Reuber, Heidi Christensen
Title Dementia detection using automatic analysis of conversations
Editor(s)
Tag(s) EMCA, In Press, Dementia, Detection, Speech recognition, Pathological speech
Publisher
Year 2018
Language English
City
Month
Journal Computer Speech & Language
Volume
Number
Pages
URL Link
DOI https://doi.org/10.1016/j.csl.2018.07.006
ISBN
Organization
Institution
School
Type
Edition
Series
Howpublished
Book title
Chapter

Download BibTex

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.

Notes