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

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dc.creatorLifchitz, Alain
dc.creatorMaire, Frederic
dc.creatorRevuz, Dominique
dc.date2006-01-25
dc.date2006-03-19
dc.date.accessioned2026-07-07T06:58:00Z
dc.date.available2026-07-07T06:58:00Z
dc.descriptionLexical 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.
dc.description5 pages, 2 figures, 4 tables
dc.identifierhttps://arxiv.org/abs/cs/0601108
dc.identifierhttp://arxiv.org/abs/cs/0601108
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/107190
dc.subjectComputer Vision and Pattern Recognition
dc.subjectArtificial Intelligence
dc.subjectData Structures and Algorithms
dc.subjectG.2.2; I.5.5; E.2
dc.titleFast Lexically Constrained Viterbi Algorithm (FLCVA): Simultaneous Optimization of Speed and Memory
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