Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Towards disorder-independent automatic assessment of emotional competence in neurological patients with a classical emotion recognition system: application in foreign accent syndrome
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-1024-168X
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Student.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Student.
Centro de Investigaciones Medico-Sanitarias, ESP.
Show others and affiliations
2021 (English)In: IEEE Transactions on Affective Computing, E-ISSN 1949-3045, Vol. 12, no 4, p. 962-973Article in journal (Refereed) Published
Abstract [en]

Emotive speech is a non-invasive and cost-effective biomarker in a wide spectrum of neurological disorders with computational systems built to automate the diagnosis. In order to explore the possibilities for the automation of a routine speech analysis in the presence of hard to learn pathology patterns, we propose a framework to assess the level of competence in paralinguistic communication. Initially, the assessment relies on a perceptual experiment completed by human listeners, and a model called the Aggregated Ear is proposed that draws a conclusion about the level of competence demonstrated by the patient. Then, the automation of the Aggregated Ear has been undertaken and resulted in a computational model that summarizes the portfolio of speech evidence on the patient. The summarizing system has a classical emotion recognition system as its central component. The code and the medical data are available from the corresponding author on request. IEEE

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2021. Vol. 12, no 4, p. 962-973
Keywords [en]
biomarker, Computational modeling, computational paralinguistics, Ear, Emotion recognition, foreign accent syndrome, health care, Neurological diseases, Pathology, Portfolios, Cost effectiveness, Diagnosis, Neurology, Speech recognition, Automatic assessment, Central component, Computational model, Computational system, Neurological disorders, Neurological patient, Summarizing systems, Speech communication
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:bth-20310DOI: 10.1109/TAFFC.2019.2908365ISI: 000722000100011Scopus ID: 2-s2.0-85089297054OAI: oai:DiVA.org:bth-20310DiVA, id: diva2:1460935
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge Foundation
Funder
Knowledge Foundation, 20140032
Note

Open access

Available from: 2020-08-25 Created: 2020-08-25 Last updated: 2023-12-05Bibliographically approved

Open Access in DiVA

Fulltext(7967 kB)360 downloads
File information
File name FULLTEXT01.pdfFile size 7967 kBChecksum SHA-512
f0557cce84d03d5a564f7bba63b2218077c1e00dec296ff7160b8744d35ab094ab6c25612aaa5780fd0bba955e16ce708b374d623bbfc0f7142dc7621ba5ec1e
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Sidorova, Julia

Search in DiVA

By author/editor
Sidorova, Julia
By organisation
Department of Computer Science
In the same journal
IEEE Transactions on Affective Computing
Language Technology (Computational Linguistics)

Search outside of DiVA

GoogleGoogle Scholar
Total: 360 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 122 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf