Automatic word recognition based on second-order hidden Markov models

We propose an extension of the Viterbi algorithm that makes second-order hidden Markov models computationally efficient. A comparative study between first-order (HMM1s) and second-order Markov models (HMM2s) is carried out. Experimental results show that HMM2s provide a better state occupancy modeling and, alone, have performances comparable with HMM1s plus postprocessing.