Label Transition and Selection Pruning and Automatic Decoding Parameter Optimization for Time-Synchronous Viterbi Decoding
Venue
13th International Conference on Document Analysis and Recognition (ICDAR), IEEE (2015), pp. 756-760
Publication Year
2015
Authors
Yasuhisa Fujii, Dmitriy Genzel, Ashok C. Popat, Remco Teunen
BibTeX
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.
