Implementing a Hidden Markov Model Speech Recognition System in Programmable Logic

Abstract

Performing Viterbi decoding for continuous real-time speech recognition is a highly computationally-demanding task, but is one which can take good advantage of a parallel processing architecture. To this end, we describe a system which uses an FPGA for the decoding and a PC for pre- and post-processing, taking advantage of the properties of this kind of programmable logic device, specifically its ability to perform in parallel the large number of additions and comparisons required. We compare the performance of the FPGA decoder to a software equivalent, and discuss issues related to this implementation.