hh.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Fusing Various Audio Feature Sets for Detection of Parkinson’s Disease from Sustained Voice and Speech Recordings
Kaunas University of Technology, Kaunas, Lithuania.
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.ORCID-id: 0000-0003-2185-8973
Kaunas University of Technology, Kaunas, Lithuania.
Kaunas University of Technology, Kaunas, Lithuania.
Visa övriga samt affilieringar
2016 (Engelska)Ingår i: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 9811, s. 328-337Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The aim of this study is the analysis of voice and speech recordings for the task of Parkinson’s disease detection. Voice modality corresponds to sustained phonation /a/ and speech modality to a short sentence in Lithuanian language. Diverse information from recordings is extracted by 22 well-known audio feature sets. Random forest is used as a learner, both for individual feature sets and for decision-level fusion. Essentia descriptors were found as the best individual feature set, achieving equal error rate of 16.3 % for voice and 13.3 % for speech. Fusion of feature sets and modalities improved detection and achieved equal error rate of 10.8 %. Variable importance in fusion revealed speech modality as more important than voice. © Springer International Publishing Switzerland 2016

Ort, förlag, år, upplaga, sidor
Heidelberg: Springer Berlin/Heidelberg, 2016. Vol. 9811, s. 328-337
Nyckelord [en]
Parkinson’s disease, Audio signal processing, OpenSMILE, Essentia, MPEG-7, jAudio, YAAFE, Random forest, Information fusion
Nationell ämneskategori
Språkteknologi (språkvetenskaplig databehandling)
Identifikatorer
URN: urn:nbn:se:hh:diva-31872DOI: 10.1007/978-3-319-43958-7_39ISI: 000389335600039Scopus ID: 2-s2.0-84984851988OAI: oai:DiVA.org:hh-31872DiVA, id: diva2:955931
Konferens
18th International Conference, SPECOM 2016, Budapest, Hungary, August 23-27, 2016
Anmärkning

Funding: Research Council of Lithuania (No. MIP-075/2015)

Tillgänglig från: 2016-08-27 Skapad: 2016-08-27 Senast uppdaterad: 2018-01-10Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Verikas, Antanas

Sök vidare i DiVA

Av författaren/redaktören
Verikas, Antanas
Av organisationen
CAISR Centrum för tillämpade intelligenta system (IS-lab)
I samma tidskrift
Lecture Notes in Computer Science
Språkteknologi (språkvetenskaplig databehandling)

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 145 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf