A statistical approach to machine translation
Computational Linguistics
C4.5: programs for machine learning
C4.5: programs for machine learning
Regular models of phonological rule systems
Computational Linguistics - Special issue on computational phonology
A design principles of a weighted finite-state transducer library
Theoretical Computer Science - Special issue on implementing automata
Multilingual Text-to-Speech Synthesis
Multilingual Text-to-Speech Synthesis
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compressed Storage of Sparse Finite-State Transducers
WIA '99 Revised Papers from the 4th International Workshop on Automata Implementation
Data-oriented methods for grapheme-to-phoneme conversion
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Automatic induction of finite state transducers for simple phonological rules
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Compilation of weighted finite-state transducers from decision trees
ACL '96 Proceedings of the 34th 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
A Computational Theory of Writing Systems (Studies in Natural Language Processing)
A Computational Theory of Writing Systems (Studies in Natural Language Processing)
Learning Local Transductions Is Hard
Journal of Logic, Language and Information
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Using finite-state automata for the text analysis component in a text-to-speech system is problematic in several respects: the rewrite rules from which the automata are compiled are difficult to write and maintain, and the resulting automata can become very large and therefore inefficient. Converting the knowledge represented explicitly in rewrite rules into a more efficient format is difficult. We take an indirect route, learning an efficient decision tree representation from data and tapping information contained in existing rewrite rules, which increases performance compared to learning exclusively from a pronunciation lexicon.