On application of the Faragó-Lugosi algorithm in speech recognition

Abstract

In 1989 a new algorithm for non-iterative global maximization of the joint likelihood function of state and observation sequences of left-right HMM's was developed by Farago and Lugosi. The algorithm capitalizes on the fact that the state sequence of a left-right HMM is uniquely determined by the state duration occupancies, The algorithm is mostly suitable for parameter estimation from a single training sequence. Extensions to estimation from multiple sequences are possible but deemed impractical. Two alternatives are proposed for utilizing this algorithm in automatic speech recognition. The first is based on averaging the parameter estimates from individual sequences while the second uses the Farago and Lugosi segmentation to initialize the segmental k-means or the Baum algorithm. We have implemented the algorithm and tested it in isolated digit recognition. Using the second approach, a reduction of the error rate from .65% to .36% was realized