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Issue No. 07 - July (2005 vol. 27)
ISSN: 0162-8828
pp: 1013-1025
Enrique Vidal , IEEE Computer Society
Francisco Casacuberta , IEEE Computer Society
Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition, and machine translation are some of them. In Part I of this paper, we survey these generative objects and study their definitions and properties. In Part II, we will study the relation of probabilistic finite-state automata with other well-known devices that generate strings as hidden Markov models and n\hbox{-}{\rm{grams}} and provide theorems, algorithms, and properties that represent a current state of the art of these objects.
Index Terms- Automata, classes defined by grammars or automata, machine learning, language acquisition, language models, language parsing and understanding, machine translation, speech recognition and synthesis, structural pattern recognition, syntactic pattern recognition.

F. Thollard, C. de la Higuera, E. Vidal, F. Casacuberta and R. C. Carrasco, "Probabilistic Finite-State Machines-Part I," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 27, no. , pp. 1013-1025, 2005.
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