A FPGA-based Viterbi algorithm implementation for speech recognition systems

This work proposes a speech recognition system based on a hardware/software co-design implementation approach. The main advantage in this approach is an expressive processing time reduction in speech recognition, because part of the system is implemented by dedicated hardware. This work also discuss another way to implement "hidden Markov models" (HMM), a probabilistic model extensively used in speech recognition systems. In this new approach, the Viterbi algorithm, used to compute the HMM likelihood score, will be "built in" together with the HMM structure designed in hardware, and implementing probabilistic state machines that will run as parallel processes one for each word in the vocabulary handled by the system. So far, we have a dramatic speed up performance, getting measures around 500 times faster than a classic implementation with the correctness comparable with others isolated word recognition systems