About: Comparing Prosody Formalisms for Machine Learning     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • We need to find the most suitable prosody formalism for the task of machine learning. The target application is a prosody generative module for text-to-speech synthesis. This module will learn prosody marks (parameters or symbols) from large corpora. Formalism we are looking for should be general, perceptually relevant, restorable, automatically obtained, objective and learnable. Main formalisms for the pitch description are briefly described and compared, namely Fujisaki model, ToBI, Intsint, Tilt and %22Glissando threshold%22 adaptation. The most suitable method of pitch description for the task of machine learning is %22Glissando threshold%22 adaptation with an additional simplification.
  • We need to find the most suitable prosody formalism for the task of machine learning. The target application is a prosody generative module for text-to-speech synthesis. This module will learn prosody marks (parameters or symbols) from large corpora. Formalism we are looking for should be general, perceptually relevant, restorable, automatically obtained, objective and learnable. Main formalisms for the pitch description are briefly described and compared, namely Fujisaki model, ToBI, Intsint, Tilt and %22Glissando threshold%22 adaptation. The most suitable method of pitch description for the task of machine learning is %22Glissando threshold%22 adaptation with an additional simplification. (en)
Title
  • Comparing Prosody Formalisms for Machine Learning
  • Comparing Prosody Formalisms for Machine Learning (en)
skos:prefLabel
  • Comparing Prosody Formalisms for Machine Learning
  • Comparing Prosody Formalisms for Machine Learning (en)
skos:notation
  • RIV/00216208:11320/07:10077941!RIV11-GA0-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET101120503), P(GD201/05/H014)
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
  • 414274
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/07:10077941
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • learning; machine; formalisms; prosody; comparing (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [A36309809F68]
http://linked.open...v/mistoKonaniAkce
  • Praha, Czechia
http://linked.open...i/riv/mistoVydani
  • Praha, Czechia
http://linked.open...i/riv/nazevZdroje
  • WDS'07 Proceedings of Contributed Papers
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
  • Raab, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Matfyzpress, Charles University
https://schema.org/isbn
  • 978-80-7378-023-4
http://localhost/t...ganizacniJednotka
  • 11320
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software