About: Czech language features selection and prosody modelling for text-to-speech synthesis     Goto   Sponge   NotDistinct   Permalink

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Description
  • Speech has probabilistic behaviour. It is very difficult to define its specific properties. In that case we can use statistic methods, e.g. Artificial Neural Networks (ANN) and General Unary Hypotheses Automaton (GUHA). In this paper, the replies to two aspects, which affect the speech naturalness, are searched. They are a selection of most important parameters, which play important role for prosody modelling. The second one is an influence of co-articulation for the fundamental frequency (F0) correct values of phonemes determination. The application of ANN for the fundamental frequency and duration (D) of phonemes modelling, the minimization of the number of input parameters, the reduction of the structure redundancy by GUHA and pruning methods and Czech synthesizer ARTIC for the result verification were used.
  • Speech has probabilistic behaviour. It is very difficult to define its specific properties. In that case we can use statistic methods, e.g. Artificial Neural Networks (ANN) and General Unary Hypotheses Automaton (GUHA). In this paper, the replies to two aspects, which affect the speech naturalness, are searched. They are a selection of most important parameters, which play important role for prosody modelling. The second one is an influence of co-articulation for the fundamental frequency (F0) correct values of phonemes determination. The application of ANN for the fundamental frequency and duration (D) of phonemes modelling, the minimization of the number of input parameters, the reduction of the structure redundancy by GUHA and pruning methods and Czech synthesizer ARTIC for the result verification were used. (en)
Title
  • Czech language features selection and prosody modelling for text-to-speech synthesis
  • Czech language features selection and prosody modelling for text-to-speech synthesis (en)
skos:prefLabel
  • Czech language features selection and prosody modelling for text-to-speech synthesis
  • Czech language features selection and prosody modelling for text-to-speech synthesis (en)
skos:notation
  • RIV/49777513:23520/03:00000011!RIV/2004/GA0/235204/N
http://linked.open.../vavai/riv/strany
  • 98-102
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/02/0124), Z(MSM 212300014)
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
  • 602627
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/03:00000011
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • text-to-speech;neural network;prosody training;prosody modelling;prosody parameter selection (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [BE3F20DCB2A2]
http://linked.open...v/mistoKonaniAkce
  • Liberec
http://linked.open...i/riv/mistoVydani
  • Liberec
http://linked.open...i/riv/nazevZdroje
  • ECMS 2003
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...ocetUcastnikuAkce
http://linked.open...nichUcastnikuAkce
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Matoušek, Jindřich
  • Tučková, Jana
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
  • Technical University
https://schema.org/isbn
  • 80-7083-708-X
http://localhost/t...ganizacniJednotka
  • 23520
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