A Coarse-to-Fine Approach to Computing the k-Best Viterbi Paths

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Abstract

The Hidden Markov Model (HMM) is a probabilistic model used widely in the fields of Bioinformatics and Speech Recognition. Efficient algorithms for solving the most common problems are well known, yet they all have a running time that is quadratic in the number of hidden states, which can be problematic for models with very large state spaces. The Viterbi algorithm is used to find the maximum likelihood hidden state sequence, and it has earlier been shown that a coarse-to-fine modification can significantly speed up this algorithm on some models. We propose combining work on a k-best version of Viterbi algorithm with the coarse-to-fine framework. This algorithm may be used to approximate the total likelihood of the model, or to evaluate the goodness of the Viterbi path on very large models.