Quality estimation of synthesized speech transmitted over IP channel using genetic programming approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @InProceedings{Mrvova:2013:DT,
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author = "Miroslava Mrvova and Peter Pocta",
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booktitle = "International Conference on Digital Technologies, DT
2013",
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title = "Quality estimation of synthesized speech transmitted
over IP channel using genetic programming approach",
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year = "2013",
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month = may,
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pages = "39--43",
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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",
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DOI = "doi:10.1109/DT.2013.6566282",
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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.",
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notes = "Also known as \cite{6566282}",
- }
Genetic Programming entries for
Miroslava Mrvova
Peter Pocta
Citations