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  • In the present contribution, I deal with the problem of improvement of synthetic speech prosody. My idea was try to possibility which classify speech signals which hold emotions for the prosody modelling. This my thought was based on the musical theory. The emotional speech analysis started treat my diploma students (see [1], [2]). We use the ANN topology, namely the multilayer neural network (MLNN) with the BPG learning algorithm and the supervised SOM (SSOM) topology for experiments. According to our experience the neural networks are able to achieve better results. The great advantage of NN is the robustness of the solution for real methods, in the area of noise signal processing especially. The first experiments are based on the idea of the musical interval.
  • In the present contribution, I deal with the problem of improvement of synthetic speech prosody. My idea was try to possibility which classify speech signals which hold emotions for the prosody modelling. This my thought was based on the musical theory. The emotional speech analysis started treat my diploma students (see [1], [2]). We use the ANN topology, namely the multilayer neural network (MLNN) with the BPG learning algorithm and the supervised SOM (SSOM) topology for experiments. According to our experience the neural networks are able to achieve better results. The great advantage of NN is the robustness of the solution for real methods, in the area of noise signal processing especially. The first experiments are based on the idea of the musical interval. (en)
Title
  • Emotions analysed by music theory.
  • Emotions analysed by music theory. (en)
skos:prefLabel
  • Emotions analysed by music theory.
  • Emotions analysed by music theory. (en)
skos:notation
  • RIV/68407700:21230/10:00174420!RIV11-GA0-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/09/0989), Z(MSM6840770012)
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
  • 257033
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/10:00174420
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Emotions; Neural networks; Musical interval. (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [C52D31E9F9F2]
http://linked.open...v/mistoKonaniAkce
  • Prague
http://linked.open...i/riv/mistoVydani
  • Prague
http://linked.open...i/riv/nazevZdroje
  • 20th Czech - German Workshop on Speech Processing
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
  • Vavřina, Josef
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Ústav fotoniky a elektroniky AV ČR
https://schema.org/isbn
  • 978-80-86269-21-4
http://localhost/t...ganizacniJednotka
  • 21230
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