Reduction of Non Deterministic Automata for Hidden Markov Model Based Pattern Recognition Applications
Identifieur interne : 002659 ( Istex/Curation ); précédent : 002658; suivant : 002660Reduction of Non Deterministic Automata for Hidden Markov Model Based Pattern Recognition Applications
Auteurs : Frederic Maire [Australie] ; Frank Wathne [Australie] ; Alain Lifchitz [France]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2003.
English descriptors
- KwdEn :
- Algorithm, Automaton, Compression algorithm, Deterministic automata, Dynamic programming, Edge automata, Equivalence, Equivalence relation, Equivalence relations, Equivalent nodes, Experimental results, Fewer labels, Frederic maire, Heuristic, Lexical constraint, Lexicon, Node, Node automata, Nodes transitions, Original automaton, Posteriori probability, Similar nodes, Speech recognition, Trie, Viterbi, Viterbi algorithm.
- Teeft :
- Algorithm, Automaton, Compression algorithm, Deterministic automata, Dynamic programming, Edge automata, Equivalence, Equivalence relation, Equivalence relations, Equivalent nodes, Experimental results, Fewer labels, Frederic maire, Heuristic, Lexical constraint, Lexicon, Node, Node automata, Nodes transitions, Original automaton, Posteriori probability, Similar nodes, Speech recognition, Trie, Viterbi, Viterbi algorithm.
Abstract
Abstract: Most on-line cursive handwriting recognition systems use a lexical constraint to help improve the recognition performance. Traditionally, the vocabulary lexicon is stored in a trie (automaton whose underlying graph is a tree). In a previous paper, we showed that non-deterministic automata were computationally more efficient than tries. In this paper, we propose a new method for constructing incrementally small non-deterministic automata from lexicons. We present experimental results demonstrating a significant reduction in the number of labels in the automata. This reduction yields a proportional speed-up in HMM based lexically constrained pattern recognition systems.
Url:
DOI: 10.1007/978-3-540-24581-0_39
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automata</term><term>Equivalence</term><term>Equivalence relation</term><term>Equivalence relations</term><term>Equivalent nodes</term><term>Experimental results</term><term>Fewer labels</term><term>Frederic maire</term><term>Heuristic</term><term>Lexical constraint</term><term>Lexicon</term><term>Node</term><term>Node automata</term><term>Nodes transitions</term><term>Original automaton</term><term>Posteriori probability</term><term>Similar nodes</term><term>Speech recognition</term><term>Trie</term><term>Viterbi</term><term>Viterbi algorithm</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Algorithm</term><term>Automaton</term><term>Compression algorithm</term><term>Deterministic automata</term><term>Dynamic programming</term><term>Edge automata</term><term>Equivalence</term><term>Equivalence relation</term><term>Equivalence relations</term><term>Equivalent nodes</term><term>Experimental results</term><term>Fewer labels</term><term>Frederic maire</term><term>Heuristic</term><term>Lexical constraint</term><term>Lexicon</term><term>Node</term><term>Node automata</term><term>Nodes transitions</term><term>Original automaton</term><term>Posteriori probability</term><term>Similar nodes</term><term>Speech recognition</term><term>Trie</term><term>Viterbi</term><term>Viterbi algorithm</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Most on-line cursive handwriting recognition systems use a lexical constraint to help improve the recognition performance. Traditionally, the vocabulary lexicon is stored in a trie (automaton whose underlying graph is a tree). In a previous paper, we showed that non-deterministic automata were computationally more efficient than tries. In this paper, we propose a new method for constructing incrementally small non-deterministic automata from lexicons. We present experimental results demonstrating a significant reduction in the number of labels in the automata. This reduction yields a proportional speed-up in HMM based lexically constrained pattern recognition systems.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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