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  • The subject of the contribution is one of application of KSOM in medical research. It behaves about of parts of the project finding connectivity between neurological disorders of children - developmental dysphasia and assessment of the degree of perception and impairment of speech. Our research is based on classification solved by SSOM with using phonetics knowledge. We use this approach for neural network ability to recognize relevant and irrelevant information about speech and minor noise dependence. The SSOM combines aspects of a vector quantization method with a topology-preserving ordering of the quantization vectors. Children speech database is away complemented. It must bee structed according to age and gender. The submitted maps trained by input data of 6 years old healthy children were compared with the input data of 6 patients observed befor and after first portion medical therapy. The assumed difference in Recognition Rate between both records was proved true.
  • The subject of the contribution is one of application of KSOM in medical research. It behaves about of parts of the project finding connectivity between neurological disorders of children - developmental dysphasia and assessment of the degree of perception and impairment of speech. Our research is based on classification solved by SSOM with using phonetics knowledge. We use this approach for neural network ability to recognize relevant and irrelevant information about speech and minor noise dependence. The SSOM combines aspects of a vector quantization method with a topology-preserving ordering of the quantization vectors. Children speech database is away complemented. It must bee structed according to age and gender. The submitted maps trained by input data of 6 years old healthy children were compared with the input data of 6 patients observed befor and after first portion medical therapy. The assumed difference in Recognition Rate between both records was proved true. (en)
Title
  • Data Mining of Children´s Speech by Growing Hierarchical Self-Organizing Map
  • Data Mining of Children´s Speech by Growing Hierarchical Self-Organizing Map (en)
skos:prefLabel
  • Data Mining of Children´s Speech by Growing Hierarchical Self-Organizing Map
  • Data Mining of Children´s Speech by Growing Hierarchical Self-Organizing Map (en)
skos:notation
  • RIV/68407700:21230/06:00126098!RIV11-MZ0-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(NR8287)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 470473
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/06:00126098
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • children´s speech; data mining; growing hierarchical self-organizing map (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [FE6DB1E4678D]
http://linked.open...v/mistoKonaniAkce
  • Žilina
http://linked.open...i/riv/mistoVydani
  • Žilina
http://linked.open...i/riv/nazevZdroje
  • Digital Technologies 2006 - 3rd International Workshop
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Tučková, Jana
  • Zetocha, Petr
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Žilinská univerzita v Žiline. Elektrotechnická fakulta
https://schema.org/isbn
  • 80-8070-637-9
http://localhost/t...ganizacniJednotka
  • 21230
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