Spoken language identification based on the transcript analysis.

Language identification is a great challenge in language engineering, which arises along with the tasks of speech recognition, machine translation, cross-language information retrieval, intelligent dialogue system creation, etc. The presented article introduces the intelligent language identificatio...

Descripción completa

Detalles Bibliográficos
Publicado en:Digital Scholarship in the Humanities Vol. 38; no. 2; pp. 586 - 596
Autores principales: Lande, Dmytro V, Dmytrenko, Olegh O, Shevchenko, Anatolij I, Klymenko, Mykyta S, Vakulenko, Maksym O
Formato: Artículo
Publicado: Oxford University Press / USA Jun2023
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=164367968&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 164367968
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        2055768X
        JEO9
      jtl: Digital Scholarship in the Humanities
      issn: 2055768X
      maglogo: N
    pubinfo:
      dt: Jun2023
      vid: 38
      iid: 2
      pid: 622
      pub: Oxford University Press / USA
    artinfo:
      ui:
        164367968
        10.1093/llc/fqac052
      ppf: 586
      ppct: 10
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
              size: 1.3MB
      tig:
        atl: Spoken language identification based on the transcript analysis.
      aug:
        au:
          Lande, Dmytro V
          Dmytrenko, Olegh O
          Shevchenko, Anatolij I
          Klymenko, Mykyta S
          Vakulenko, Maksym O
        affil:
          Institute of Information Recording Problems , Kyïv, Ukraine
          National Technical University of Ukraine "Igor Sikorsky Kyïv Polytechnic Institute" , Kyïv, Ukraine
          Institute of Problems of Artificial Intelligence , Kyïv, Ukraine
          State Scientific and Technical Library of Ukraine , Kyïv, Ukraine
      su:
        Cross-language information retrieval
        Oral communication
        Automatic speech recognition
        Speech perception
        Machine translating
        Recognition (Psychology)
      sug:
        subj:
          Cross-language information retrieval
          Oral communication
          Automatic speech recognition
          Speech perception
          Machine translating
          Recognition (Psychology)
      ab: Language identification is a great challenge in language engineering, which arises along with the tasks of speech recognition, machine translation, cross-language information retrieval, intelligent dialogue system creation, etc. The presented article introduces the intelligent language identification technology, which is based on speech recognition and statistical methods of spectrogram analysis. The approach to the automatic identification of the spoken language sample uploaded to the system, in particular from video streaming services such as YouTube, is put forward. The article focuses on the automatic identification of spoken language, taking into account several speech recognition solutions for correct or incorrect speech recognition and its conversion into correct or incorrect text. The obtained algorithm is demonstrated in the Ukrainian and Russian languages. The identification quality of the language of an utterance, which lasts >30 s is almost 100%, and for the utterance of a duration of 30 s, the quality is 98%, and for the 5-s utterance, it reaches 89.6%. In addition to that, the system performance is contingent on the streaming speed, so it is a real-time system.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      custom: © 2019 EADH: The European Association for Digital Humanities.
      item: Digital Scholarship in the Humanities
      holder: Oxford University Press / USA
      dt:
        @attributes:
          year: 2023
    holdings:
      @attributes:
        islocal: N