hh.sePublications
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 Voice and Query Data-based Non-invasive Screening for Laryngeal Disorders
Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.ORCID iD: 0000-0003-2185-8973
Kaunas University of Technology, Kaunas, Lithuania.
Kaunas University of Technology, Kaunas, Lithuania.
Show others and affiliations
2015 (English)In: Advances in Electrical and Computer Engineering: Proceedings of the 17th International Conference on Automatic Control, Modelling & Simulation (ACMOS '15): Proceedings of the 14th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED '15): Proceedings of the 6th International Conference on Circuits, Systems, Control, Signals (CSCS '15): Tenerife, Canary Islands, Spain, January 10-12, 2015 / [ed] Nikos E. Mastorakis & Imre J. Rudas, Athens: WSEAS Press , 2015, 32-39 p.Conference paper, Published paper (Refereed)
Abstract [en]

Topic of the research is exploration and fusion of non-invasive measurements for an accurate detection of pathological larynx. Measurements for human subject encompass results of a specific survey and information extracted by openSMILE toolkit from several audio recordings of sustained phonation (vowel/a/). Clinical diagnosis, assigned by medical specialist, is a target attribute for binary classification into healthy and pathological cases. Random forest (RF) is used here as a base-learner and also as a meta-learner for decision-level fusion. Fusion combines decisions from ensemble of 5 RF classifiers built on 3 variants of audio recording data (raw and after two types of voice activity detection) and 2 variants of questionnaire (with 9 and 26 questions) data. Out-of-bag equal error rate (EER) was found to be higher for audio data and lower for querry, but each variant was outperformed by the fusion where the lowest EER of 4.8% was achieved. Finally, due to noteworthy performance of the querry data, 22 association rules (11 healthy + 11 pathological) using 17 questions were obtained for comprehensible insights.

Place, publisher, year, edition, pages
Athens: WSEAS Press , 2015. 32-39 p.
Keyword [en]
Ensemble methods, random forest, affinity analysis voice activity detection, voice pathology detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-29333ISBN: 978-1-61804-279-8 OAI: oai:DiVA.org:hh-29333DiVA: diva2:850094
Conference
The 14th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED’15), Tenerife, Canary Islands, Spain, January 10-12, 2015
Note

This study was supported by a grant VP1-3.1-ŠMM-10-V-02-030 from the Ministry of Education and Science of Republic of Lithuania.

Available from: 2015-08-31 Created: 2015-08-31 Last updated: 2015-09-04Bibliographically approved

Open Access in DiVA

No full text

Other links

Full text

Search in DiVA

By author/editor
Verikas, AntanasHållander, Magnus
By organisation
CAISR - Center for Applied Intelligent Systems Research
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

Total: 140 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