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Speech Recognition by Composition of Weighted Finite Automata
 FINITESTATE LANGUAGE PROCESSING
, 1996
"... We present a general framework based on weighted finite automata and weighted finitestate transducers for describing and implementing speech recognizers. The framework allows us to represent uniformly the information sources and data structures used in recognition, including contextdependent u ..."
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Cited by 124 (12 self)
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We present a general framework based on weighted finite automata and weighted finitestate transducers for describing and implementing speech recognizers. The framework allows us to represent uniformly the information sources and data structures used in recognition, including contextdependent units, pronunciation dictionaries, language models and lattices. Furthermore, general but efficient algorithms can used for combining information sources in actual recognizers and for optimizing their application. In particular, a single composition algorithm is used both to combine in advance information sources such as language models and dictionaries, and to combine acoustic observations and information sources dynamically during recognition.
Robust Grammatical Analysis for Spoken Dialogue Systems
 Natural Language Engineering
, 1997
"... We argue that grammatical analysis is a viable alternative to concept spotting for processing spoken input in a practical spoken dialogue system. We discuss the structure of the grammar, and a model for robust parsing which combines linguistic sources of information and statistical sources of inform ..."
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Cited by 50 (8 self)
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We argue that grammatical analysis is a viable alternative to concept spotting for processing spoken input in a practical spoken dialogue system. We discuss the structure of the grammar, and a model for robust parsing which combines linguistic sources of information and statistical sources of information. We discuss test results suggesting that grammatical processing allows fast and accurate processing of spoken input.
Weighted rational transductions and their application to human language processing
 In ARPA Workshop on Human Language Technology
, 1994
"... We present the concepts of weighted language, ~ansduction and automaton from algebraic automata theory as a general framework for describing and implementing decoding cascades in speech and language processing. This generality allows us to represent uniformly such information sources as pronunciat ..."
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Cited by 50 (8 self)
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We present the concepts of weighted language, ~ansduction and automaton from algebraic automata theory as a general framework for describing and implementing decoding cascades in speech and language processing. This generality allows us to represent uniformly such information sources as pronunciation dictionaries, language models artd lattices, and to use uniform algorithms for building decoding stages and for optimizing and combining them. In particular, a single automata join algorithm can be used either to combine information sources such as a pronunciation dictionary and a contextdependency model during the construction of a decoder, or dynamically during the operation of the decoder. Applications to speech recognition and to Chinese text segmentation will be discussed. 1.
Recognition can be Harder than Parsing
 Computational Intelligence
, 1992
"... this paper is to discuss the scope and limitations of this approach, and to examine the suitability of several syntactic formalisms on the criterion of their ability to handle it. 2 Parsing as intersection ..."
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Cited by 39 (0 self)
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this paper is to discuss the scope and limitations of this approach, and to examine the suitability of several syntactic formalisms on the criterion of their ability to handle it. 2 Parsing as intersection
An Efficient Implementation of the HeadCorner Parser
 COMPUTATIONAL LINGUISTICS
, 1996
"... This paper describes an efficient and robust implementation of a bidirectional, headdriven parser for constraintbased grammars. This parser is developed for the OVIS system: a Dutch spoken dialogue system in which information about public transport can be obtained by telephone. After a Review ..."
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Cited by 36 (2 self)
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This paper describes an efficient and robust implementation of a bidirectional, headdriven parser for constraintbased grammars. This parser is developed for the OVIS system: a Dutch spoken dialogue system in which information about public transport can be obtained by telephone. After a Review
FSA Utilities: A Toolbox to Manipulate Finitestate Automata
 Automata Implementation
, 1997
"... This paper describes the FSA Utilities toolbox: a collection of utilities to manipulate finitestate automata and finitestate transducers. Manipulations include determinization (both for finitestate acceptors and finitestate transducers), minimization, composition, complementation, intersection, ..."
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Cited by 23 (3 self)
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This paper describes the FSA Utilities toolbox: a collection of utilities to manipulate finitestate automata and finitestate transducers. Manipulations include determinization (both for finitestate acceptors and finitestate transducers), minimization, composition, complementation, intersection, Kleene closure, etc. Furthermore, various visualization tools are available to browse finitestate automata. The toolbox is implemented in SICStus Prolog.
The intersection of Finite State Automata and Definite Clause Grammars
, 1995
"... Bernard Lang defines parsing as the calculation of the intersection of a FSA (the input) and a CFG. Viewing the input for parsing as a FSA rather than as a string combines well with some approaches in speech understanding systems, in which parsing takes a word lattice as input (rather than a word st ..."
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Cited by 20 (6 self)
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Bernard Lang defines parsing as the calculation of the intersection of a FSA (the input) and a CFG. Viewing the input for parsing as a FSA rather than as a string combines well with some approaches in speech understanding systems, in which parsing takes a word lattice as input (rather than a word string). Furthermore, certain techniques for robust parsing can be modelled as finite state transducers.
Robust Efficient Parsing for Spoken Dialogue Processing
, 1998
"... ion (Johnson and Dorre, [39]) ffl x(A,B,f(A,B),g(A,h(B,i(C)))) =) x(A,B,f(,),g(,)) ffl parsewithweakening(Cat,P0,P,E0,E) : weaken(Cat,WeakenedCat), parse(WeakenedCat,P0,P,E0,E), Cat=WeakenedCat. ffl Really helps! Ambiguity Packing ffl A parser should not construct all parse trees (exponential) ..."
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ion (Johnson and Dorre, [39]) ffl x(A,B,f(A,B),g(A,h(B,i(C)))) =) x(A,B,f(,),g(,)) ffl parsewithweakening(Cat,P0,P,E0,E) : weaken(Cat,WeakenedCat), parse(WeakenedCat,P0,P,E0,E), Cat=WeakenedCat. ffl Really helps! Ambiguity Packing ffl A parser should not construct all parse trees (exponential) ffl Instead, a compact representation of all such parse trees are constructed  grammar [42, 9]  parse forest [76]  packed structures [3] ffl Here: for every `result item' keep track of the lexical entry and references of other result items that were used to create it ffl Results in a lexicalized tree substitution grammar ffl which generates the input sentence with all its parse trees Bottomup Inactivechart Parser Item form: [i;X; j] Axioms: Goals: [0;S;n] Inference Rules: Scan [q i ;wi; qi+1 ] Complete [q k ;X1; q k 0][q k 0;X2; q k 00] : : : [q m0;Xl; qm] [q k ;X0; qm] X0 !X1:::Xl Bottomup Inactivechart Parser Inference Rules: Scan [q i ;wi; qi+...