Robust parametric modeling of durations in hidden Markov models

We address the problem of explicit state and word duration modeling in hidden Markov models (HMMs). A major weakness of conventional HMMs is that they implicitly model state durations by a geometric distribution, which is usually inappropriate. Using explicit modeling of state and word durations, it is possible to significantly enhance the performance of speech recognition systems. The main outcome of this work is a modified Viterbi algorithm that by incorporating both state and word duration modeling, reduces the string error rate of the conventional Viterbi algorithm by 29% and 43% for known and unknown string lengths respectively, for a speaker independent, connected digit string task. The uniqueness of the algorithm is that unlike alternative approaches, it adds the duration metric at each frame transition (and not at the end of a state, word or sentence), thus enhancing the performance.