Quality estimation of synthesized speech transmitted over IP channel using genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Mrvova:2013:DT,
  author =       "Miroslava Mrvova and Peter Pocta",
  booktitle =    "International Conference on Digital Technologies, DT
                 2013",
  title =        "Quality estimation of synthesized speech transmitted
                 over IP channel using genetic programming approach",
  year =         "2013",
  month =        may,
  pages =        "39--43",
  keywords =     "genetic algorithms, genetic programming, IP networks,
                 Internet telephony, learning (artificial intelligence),
                 speech synthesis, voice communication, GP approach, IP
                 channel, VoIP environment, evolutionary algorithm,
                 generalisation ability, genetic programming approach,
                 good accuracy, machine learning techniques, parametric
                 speech quality estimation model, quality-affecting
                 parameters, synthesised speech, telecommunication
                 services, Databases, Estimation, Mathematical model,
                 Sociology, Speech, Speech coding, packet loss, speech
                 codec, speech quality estimation, synthesised speech",
  DOI =          "doi:10.1109/DT.2013.6566282",
  abstract =     "In this article, an evolutionary algorithm known as
                 Genetic Programming (GP) was used to design a
                 parametric speech quality estimation model. Nowadays,
                 GP is one of the machine learning techniques employed
                 in a quality estimation process. In principle, the set
                 of quality-affecting parameters was used as an input to
                 the designed estimation model based on GP approach in
                 order to estimate a quality of synthesised speech
                 transmitted over IP channel (VoIP environment). The
                 performance results obtained by the designed estimation
                 model have confirmed the good properties of genetic
                 programming, namely good accuracy and generalisation
                 ability; this makes it to be perspective approach to a
                 quality estimation of this type of speech in the
                 corresponding environment. The developed model can be
                 helpful for network operators and service providers
                 implementing it in planning phase or early-development
                 stage of telecommunication services based on
                 synthesised speech.",
  notes =        "Also known as \cite{6566282}",
}

Genetic Programming entries for Miroslava Mrvova Peter Pocta

Citations