Speech Recognition on an FPGA Using Discrete and Continuous Hidden Markov Models

  • Stephen J. Melnikoff
  • 1Steven F. Quigley
  • Martin J. Russell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2438)


Speech recognition is a computationally demanding task, particularly the stage which uses Viterbi decoding for converting pre-processed speech data into words or sub-word units. Any device that can reduce the load on, for example, a PC’s processor, is advantageous. Hence we present FPGA implementations of the decoder based alternately on discrete and continuous hidden Markov models (HMMs) representing monophones, and demonstrate that the discrete version can process speech nearly 5,000 times real time, using just 12% of the slices of a Xilinx Virtex XCV1000, but with a lower recognition rate than the continuous implementation, which is 75 times faster than real time, and occupies 45% of the same device.


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  1. 1.
    Burchard, B. & Romer, R., “A single chip phoneme based HMM speech recognition system for consumer applications,” IEEE Trans. Consumer Elec., 46, No.3, 2000, pp.914–919.CrossRefGoogle Scholar
  2. 2.
    Gorin, A.L., Riccardi, G. & Wright, J.H., “How may I help you?” Speech Communication, 23, 1997, pp.113–127.CrossRefGoogle Scholar
  3. 3.
    Holmes, J. N. & Holmes WJ, “Speech synthesis and recognition,” Taylor & Francis, 2001Google Scholar
  4. 4.
    Melnikoff, S.J., James-Roxby, P.B., Quigley, S.F. & Russell, M.J., “Reconfigurable computing for speech recognition: preliminary findings,” FPL 2000, LNCS #1896, 2000, pp.495–504.Google Scholar
  5. 5.
    Melnikoff, S.J., Quigley, S.F. & Russell, M.J., “Implementing a hidden Markov model speech recognition system in programmable logic,” FPL 2001, LNCS #2147, 2001, pp.81–90.Google Scholar
  6. 6.
    Nakamura K. et al, “Speech recognition chip for monosyllables,” Proc. Asia and South Pacific Design Automation Conference (ASP-DAC 2001), IEEE, 2001, pp.396–399.Google Scholar
  7. 7.
    Rabiner, L.R., “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE, 77, No.2, 1989, pp.257–286.Google Scholar
  8. 8.
    Shi Y.Y., Liu J. & Liu R.S., “Single-chip speech recognition system based on 8051 microcontroller core,” IEEE Trans. Consumer Elec., 47, No.1, 2001, pp.149–153.CrossRefGoogle Scholar
  9. 9.
    Shozakai, M., “Speech interface VLSI for car applications”, ICASSP’ 99, 1999, pp.141–144.Google Scholar
  10. 10.
    Stogiannos, P., Dollas, A. & Digalakis, V., “A configurable logic based architecture for real-time continuous speech recognition using hidden Markov models,” Journal of VLSI Signal Processing Systems, 2000, 24, No.2–3, pp.223–240.CrossRefGoogle Scholar
  11. 11.
    Woodland, P.C., Odell, J.J., Valtchev, V. & Young, S.J. “Large vocabulary continuous speech recognition using HTK,” ICASSP’ 94, 1994, pp.125–128.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Stephen J. Melnikoff
    • 1
  • 1Steven F. Quigley
  • Martin J. Russell
    • 1
  1. 1.Electronic, Electrical and Computer EngineeringUniversity of BirminghamEdgbaston, BirminghamUK

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