Fundamentals of speech recognition
Fundamentals of speech recognition
Context-free languages and pushdown automata
Handbook of formal languages, vol. 1
Minimization algorithms for sequential transducers
Theoretical Computer Science
Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text
Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text
Automata: Theoretic Aspects of Formal Power Series
Automata: Theoretic Aspects of Formal Power Series
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
The theory of parsing, translation, and compiling
The theory of parsing, translation, and compiling
A Rational Design for a Weighted Finite-State Transducer Library
WIA '97 Revised Papers from the Second International Workshop on Implementing Automata
Finite-state transducers in language and speech processing
Computational Linguistics
A finite-state parser for use in speech recognition
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
An efficient compiler for weighted rewrite rules
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Practical experiments with regular approximation of context-free languages
Computational Linguistics - Special issue on finite-state methods in NLP
fsm2 - a scripting language interpreter for manipulating weighted finite-state automata
FSMNLP'09 Proceedings of the 8th international conference on Finite-state methods and natural language processing
Compiling linguistic constraints into finite state automata
CIAA'06 Proceedings of the 11th international conference on Implementation and Application of Automata
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Weighted context-free grammars are a convenient formalism for representing grammatical constructions and their likelihoods in a variety of language-processing applications. In particular, speech understanding applications require appropriate grammars both to constrain speech recognition and to help extract the meaning of utterances. In many of those applications, the actual languages described are regular, but context-free representations are much more concise and easier to create. We describe an efficient algorithm for compiling into weighted finite automata an interesting class of weighted context-free grammars that represent regular languages. The resulting automata can then be combined with other speech recognition components. Our method allows the recognizer to dynamically activate or deactivate grammar rules and substitute a new regular language for some terminal symbols, depending on previously recognized inputs, all without recompilation. We also report experimental results showing the practicality of the approach.