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)

Abstract

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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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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