• DocumentCode
    1531996
  • Title

    The ICSI RT-09 Speaker Diarization System

  • Author

    Friedland, Gerald ; Janin, Adam ; Imseng, David ; Miro, Xavier Anguera ; Gottlieb, Luke ; Huijbregts, Marijn ; Knox, Mary Tai ; Vinyals, Oriol

  • Author_Institution
    Int. Comput. Sci. Inst., Berkeley, CA, USA
  • Volume
    20
  • Issue
    2
  • fYear
    2012
  • Firstpage
    371
  • Lastpage
    381
  • Abstract
    The speaker diarization system developed at the International Computer Science Institute (ICSI) has played a prominent role in the speaker diarization community, and many researchers in the rich transcription community have adopted methods and techniques developed for the ICSI speaker diarization engine. Although there have been many related publications over the years, previous articles only presented changes and improvements rather than a description of the full system. Attempting to replicate the ICSI speaker diarization system as a complete entity would require an extensive literature review, and might ultimately fail due to component description version mismatches. This paper therefore presents the first full conceptual description of the ICSI speaker diarization system as presented to the National Institute of Standards Technology Rich Transcription 2009 (NIST RT-09) evaluation, which consists of online and offline subsystems, multi-stream and single-stream implementations, and audio and audio-visual approaches. Some of the components, such as the online system, have not been previously described. The paper also includes all necessary preprocessing steps, such as Wiener filtering, speech activity detection and beamforming.
  • Keywords
    speech processing; ICSI RT-09 speaker diarization system; International Computer Science Institute; NIST RT-09 evaluation; National Institute of Standards Technology Rich Transcription 2009 evaluation; Wiener filtering; beamforming; conceptual description; multistream implementation; offline subsystem; online subsystem; single-stream implementation; speech activity detection; Channel estimation; Data models; Delay; Hidden Markov models; Mel frequency cepstral coefficient; Microphones; Speech; Gaussian mixture models (GMMs); machine learning; speaker diarization;
  • fLanguage
    English
  • Journal_Title
    Audio, Speech, and Language Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1558-7916
  • Type

    jour

  • DOI
    10.1109/TASL.2011.2158419
  • Filename
    5783332