• DocumentCode
    270244
  • Title

    Barista: A framework for concurrent speech processing by usc-sail

  • Author

    Can, Doğan ; Gibson, J. ; Vaz, C. ; Georgiou, Panayiotis G. ; Narayanan, Shrikanth S.

  • Author_Institution
    Signal Anal. & Interpretation Lab., Univ. of Southern California, Los Angeles, CA, USA
  • fYear
    2014
  • fDate
    4-9 May 2014
  • Firstpage
    3306
  • Lastpage
    3310
  • Abstract
    We present Barista, an open-source framework for concurrent speech processing based on the Kaldi speech recognition toolkit and the libcppa actor library. With Barista, we aim to provide an easy-to-use, extensible framework for constructing highly customizable concurrent (and/or distributed) networks for a variety of speech processing tasks. Each Barista network specifies a flow of data between simple actors, concurrent entities communicating by message passing, modeled after Kaldi tools. Leveraging the fast and reliable concurrency and distribution mechanisms provided by libcppa, Barista lets demanding speech processing tasks, such as real-time speech recognizers and complex training workflows, to be scheduled and executed on parallel (and/or distributed) hardware. Barista is released under the Apache License v2.0.
  • Keywords
    message passing; public domain software; speech recognition; Apache License v2.0; Barista network; Kaldi speech recognition toolkit; complex training workflows; concurrent entities; concurrent speech processing; data flow; libcppa actor library; message passing; open-source framework; real-time speech recognizers; usc-sail; Computational modeling; Decoding; Feature extraction; Message systems; Speech; Speech processing; Speech recognition; C++; actor model; concurrency and distribution; open source; real-time speech recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
  • Conference_Location
    Florence
  • Type

    conf

  • DOI
    10.1109/ICASSP.2014.6854212
  • Filename
    6854212