• DocumentCode
    3513278
  • Title

    Low power embedded speech recognition system based on a MCU and a coprocessor

  • Author

    Peng Li ; Tang, Hua ; Liang, Weiqian

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Duluth, MN
  • fYear
    2009
  • fDate
    19-24 April 2009
  • Firstpage
    625
  • Lastpage
    628
  • Abstract
    In speech recognition systems, CHMM (Continuous Hidden Markov Model) based speech recognition algorithms have the best accuracy but with the most computational cost. Neither General Purpose Processor (GPP) nor dedicated hardware implementation is a good solution for the algorithm, due to high power consumption for the former and lack of flexibility for the later. To reduce power consumption and enhance flexibility, this paper presents a speech recognition system composed of a coprocessor and a MCU. The coprocessor is a dedicated hardware design for Output Probability Calculation (OPC), which is the most computation-intensive part in CHMM, and MCU is a 32 bit RISC (ARM). Tested with a 358-state 3-mixture 27-feature 800-word HMM, MCU operates at 40 MHz and coprocessor operates at 10 MHz to meet real-time requirement. The power consumption of MCU is 10 mW, and coprocessor 1.8 mW.
  • Keywords
    coprocessors; hidden Markov models; speech recognition; continuous hidden Markov model; coprocessor; dedicated hardware design; general purpose processor; output probability calculation; power consumption; speech recognition system; Algorithm design and analysis; Coprocessors; Embedded computing; Energy consumption; Field programmable gate arrays; Hardware; Hidden Markov models; Power engineering and energy; Speech recognition; Table lookup; Coprocessors; FPGA; HMM; Speech recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
  • Conference_Location
    Taipei
  • ISSN
    1520-6149
  • Print_ISBN
    978-1-4244-2353-8
  • Electronic_ISBN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2009.4959661
  • Filename
    4959661