• DocumentCode
    3020282
  • Title

    Survey of Automated Speaker Identification Methods

  • Author

    Sidorov, Maxim ; Schmitt, Andreas ; Zablotskiy, Sergey ; Minker, Wolfgang

  • Author_Institution
    Inst. of Commun. Eng., Univ. of Ulm, Ulm, Germany
  • fYear
    2013
  • fDate
    16-17 July 2013
  • Firstpage
    236
  • Lastpage
    239
  • Abstract
    In this paper we present an overview of state-of-the-art approaches for speaker identification. Due to the increased number of dialogue system applications the interest in that field has grown significantly in recent years. Nevertheless, there are many open issues in the field of automatic speaker identification. Among them the choice of the appropriate speech signal features and machine learning algorithms could be mentioned. We make here an overview of modern methods designed for the problem of speaker identification. We also describe here our direction for possible improvements to the automated speaker identification.
  • Keywords
    learning (artificial intelligence); speaker recognition; automated speaker identification; dialogue system application; machine learning algorithm; speech signal features; Accuracy; Databases; Mel frequency cepstral coefficient; Speech; Support vector machines; Testing; Training; Gaussian mixture models; machine learning algorithms; speaker identification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Environments (IE), 2013 9th International Conference on
  • Conference_Location
    Athens
  • Type

    conf

  • DOI
    10.1109/IE.2013.31
  • Filename
    6597817