Cite this DOI

Longman Publishers (2020). Language to language Translation using GRU method. *Journal of Science & Technology*, *5*(3), 192–194. https://doi.org/10.46243/jst.2020.v5.i3.pp192-194

@article{anon2020language,
  title     = {{Language to language Translation using GRU method}},
  journal   = {Journal of Science \& Technology},
  year      = {2020},
  month     = {may},
  volume    = {5},
  number    = {3},
  pages     = {192--194},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2020.v5.i3.pp192-194},
  url       = {https://doi.org/10.46243/jst.2020.v5.i3.pp192-194},
  language  = {en},
  abstract  = {Current state of the art translation systems for speech to speech rely heavily on a text representation for the interpretation. By transcoding speech to text we lose important information about the characteristics of the voice like the emotion, pitch and accent. The thesis examine the likelihood of using an GRU neural network model to translate speech to speech without the requirement of a text representation that's by translating using the raw audio data directly so as to persevere the characteristics of the voice that otherwise stray within the text transcoding a part of the interpretation process. As a part of the research we create an information set of phrases suitable for speech to speech translation tasks. The thesis leads to a signal of concept system which requires scaling the underlying deep neural network so as to figure better.}
}

TY  - JOUR
TI  - Language to language Translation using GRU method
JO  - Journal of Science & Technology
PY  - 2020
DA  - 2020/05/29/
VL  - 5
IS  - 3
SP  - 192
EP  - 194
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Current state of the art translation systems for speech to speech rely heavily on a text representation for the interpretation. By transcoding speech to text we lose important information about the characteristics of the voice like the emotion, pitch and accent. The thesis examine the likelihood of using an GRU neural network model to translate speech to speech without the requirement of a text representation that's by translating using the raw audio data directly so as to persevere the characteristics of the voice that otherwise stray within the text transcoding a part of the interpretation process. As a part of the research we create an information set of phrases suitable for speech to speech translation tasks. The thesis leads to a signal of concept system which requires scaling the underlying deep neural network so as to figure better.
DO  - 10.46243/jst.2020.v5.i3.pp192-194
UR  - https://doi.org/10.46243/jst.2020.v5.i3.pp192-194
ER  -

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    "type": "article-journal",
    "id": "10.46243/jst.2020.v5.i3.pp192-194",
    "DOI": "10.46243/jst.2020.v5.i3.pp192-194",
    "URL": "https://doi.org/10.46243/jst.2020.v5.i3.pp192-194",
    "title": "Language to language Translation using GRU method",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "issued": {
        "date-parts": [
            [
                2020,
                5,
                29
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    "volume": "5",
    "issue": "3",
    "page": "192-194",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Current state of the art translation systems for speech to speech rely heavily on a text representation for the interpretation. By transcoding speech to text we lose important information about the characteristics of the voice like the emotion, pitch and accent. The thesis examine the likelihood of using an GRU neural network model to translate speech to speech without the requirement of a text representation that's by translating using the raw audio data directly so as to persevere the characteristics of the voice that otherwise stray within the text transcoding a part of the interpretation process. As a part of the research we create an information set of phrases suitable for speech to speech translation tasks. The thesis leads to a signal of concept system which requires scaling the underlying deep neural network so as to figure better.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2020.v5.i3.pp192-194 gives all four in one JSON answer.