Fast Lexically Constrained Viterbi Algorithm (FLCVA): Simultaneous Optimization of Speed and Memory

TL;DR

This paper introduces a fast, memory-efficient Viterbi algorithm for speech and handwriting recognition that simultaneously optimizes speed and memory by sharing computations across lexicon words using a token passing method.

Contribution

The paper presents a novel token passing method with an optimal coding scheme that enhances the speed and reduces memory usage in lexically constrained recognition systems.

Findings

Achieved significant speed improvements in recognition tasks.

Reduced memory requirements through optimal token coding.

Demonstrated effectiveness on speech and handwriting systems.

Abstract

Lexical constraints on the input of speech and on-line handwriting systems improve the performance of such systems. A significant gain in speed can be achieved by integrating in a digraph structure the different Hidden Markov Models (HMM) corresponding to the words of the relevant lexicon. This integration avoids redundant computations by sharing intermediate results between HMM's corresponding to different words of the lexicon. In this paper, we introduce a token passing method to perform simultaneously the computation of the a posteriori probabilities of all the words of the lexicon. The coding scheme that we introduce for the tokens is optimal in the information theory sense. The tokens use the minimum possible number of bits. Overall, we optimize simultaneously the execution speed and the memory requirement of the recognition systems.