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

Alain Lifchitz (1) , Frederic Maire (2) , Dominique Revuz (3)

1 MALIRE - Machine Learning and Information Retrieval
2 School of Computing Science, Queensland University of Technology
3 LIGM - Laboratoire d'Informatique Gaspard-Monge

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.

Mots clés: Handwriting Recognition; Large Vocabularies; DAWG; Viterbi algorithm; Optimal path history; HMM

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]; Algorithme et structure de données [cs.DS]

Alain Lifchitz, Frederic Maire, Dominique Revuz. Fast Lexically Constrained Viterbi Algorithm (FLCVA): Simultaneous Optimization of Speed and Memory. 2006. ⟨hal-00018116⟩