AI

Label Transition and Selection Pruning and Automatic Decoding Parameter Optimization for Time-Synchronous Viterbi Decoding

Abstract

Hidden Markov Model (HMM)-based classifiers have been successfully used for sequential labeling problems such as speech recognition and optical character recognition for decades. They have been especially successful in the domains where the segmentation is not known or difficult to obtain, since, in principle, all possible segmentation points can be taken into account. However, the benefit comes with a non-negligible computational cost. In this paper, we propose simple yet effective new pruning algorithms to speed up decoding with HMM-based classifiers of up to 95% relative over a baseline. As the number of tunable decoding parameters increases, it becomes more difficult to optimize the parameters for each configuration. We also propose a novel technique to estimate the parameters based on a loss value without relying on a grid search.