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Cited by 123semigroups and automata theoryNatural Language Processing TechniquesSpeech Recognition and Synthesis

The DOI matches its Crossref record.

A rational design for a weighted finite-state transducer library

Mohri, Mehryar, Pereira, Fernando, Riley, Michael·1998·Lecture Notes in Computer Science
DOI 10.1007/bfb0031388
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Crossref · 10.1007/bfb0031388

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  • If the probabilistic assumptions in an HMM or other probabilistic model are not realistic, discriminative training can improve performance, as shown for speech recognition systems.

    “On the other hand, if the probabilistic assumptions in an HMM (or other probabilistic model) are not realistic, discriminative training, discussed in Section VI, can improve performance as this has been clearly shown for speech recognition systems [48]- [50], [107], [108].”

    From Gradient-based learning applied to document recognition (1998) · cites this paper as [107]

Citations

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Research works printed but not linked: 22

  1. A. V. Aho, J. E. Hopcroft, and J. D. Ullman. The design and analysis of computer algorithms. Addison Wesley: Reading, MA, 1974.
  2. A. V. Aho, R. Sethi, and J. D. Ullman. Compilers: Principles, Techniques and Tools. Addison Wesley: Reading, MA, 1986.
  3. J. Berstel and C. Reutenauer. Rational Series and Their Languages. Springer-Verlag: Berlin-New York, 1988. · DOI
  4. T. Cormen, C. Leiserson, and R. Rivest. Introduction to Algorithms. The MIT Press: Cambridge, MA, 1992.
  5. K. Culik II and J. Kari. Digital images and formal languages. In G. Rozenberg and A. Salomaa, editors, Handbook of Formal Languages, pages 599–616. Springer, 1997. · DOI
  6. S. Eilenberg. Automata, Languages and Machines, volume A. Academic Press, 1974–1976.
  7. R. M. Kaplan and M. Kay. Regular models of phonological rule systems. Computational Linguistics, 20(3), 1994.
  8. L. Karttunen. The replace operator. In 33rd Annual Meeting of the Association for Computational Linguistics, pages 16–23. Association for Computational Linguistics, 1995. Distributed by Morgan Kaufmann Publishers, San Francisco, California. · DOI
  9. W. Kuich and A. Salomaa. Semirings, Automata, Languages. Number 5 in EATCS Monographs on Theoretical Computer Science. Springer-Verlag, Berlin, Germany, 1986. · DOI
  10. K.-F. Lee. Context dependent phonetic hidden Markov models for continuous speech recognition. IEEE Trans. ASSP, 38(4):599–609, Apr. 1990. · DOI
  11. A. Ljolje and M. D. Riley. Optimal speech recognition using phone recognition and lexical access. In Proceedings of ICSLP, pages 313–316, Banff, Canada, Oct. 1992. · DOI
  12. M. Mohri. Minimization of sequential transducers. Lecture Notes in Computer Science, 807, 1994. · DOI
  13. M. Mohri. Syntactic analysis by local grammars automata: an efficient algorithm. In Proceedings of the International Conference on Computational Lexicography (COMPLEX 94). Linguistic Institute, Hungarian Academy of Science: Budapest, Hungary, 1994.
  14. M. Mohri. On some applications of finite-state automata theory to natural language processing. Journal of Natural Language Engineering, 2:1–20, 1996. · DOI
  15. M. Mohri, F. C. N. Pereira, and M. Riley. Weighted automata in text and speech processing. In ECAI-96 Workshop, Budapest, Hungary. ECAI, 1996.
  16. M. Mohri and R. Sproat. An efficient compiler for weighted rewrite rules. In 34th Meeting of the Association for Computational Linguistic s (A CL 96), Proceedings of the Conference, Santa Cruz, California. ACL, 1996. · DOI
  17. D. Musser and A. Saini. STL Tutorial and Reference Guide. Addison-Wesley, 1996.
  18. D. Revuz. Minimisation of acyclic deterministic automata in linear time. Theoretical Computer Science, 92:181–189, 1992. · DOI
  19. G. Riccardi, E. Bocchieri, and R. Pieraccini. Non-deterministic stochastic language models for speech recognition. In Proceedings IEE International Conference on Acoustics, Speech and Signal Processing, volume 1, pages 237–240. IEEE, 1995. · DOI
  20. M. P. Schützenberger. On the definition of a family of automata. Information and Control, 4, 1961. · DOI
  21. I. Simon. Limited subsets of a free monoid. In Proceedings of the 19th Annual Symposium on Foundation of Computer Science, pages 143–150, 1978. · DOI
  22. R. Sproat, C. Shih, W. Gale, and N. Chang. A stochastic finite-state wordsegmentation algorithm for Chinese. In 32nd Annual Meeting of the Association for Computational Linguistics, pages 66–73, San Francisco, California, 1994. New Mexico State University, Las Cruces, New Mexico, Morgan Kaufmann. · DOI

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Metadata

Authors
Mohri, M., Pereira, F., & Riley, M.
Affiliations
AT&T (United States) (US)
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Berlin Heidelberg
Published
1998
Pages
144-158
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/bfb0031388
OpenAlex
W1654022110
Cited by
123
References
0
Citations by year
2024: 2 · 2017: 1 · 2015: 2 · 2014: 2 · 2013: 2 · 2012: 4
FWCI
7.48
Type
conference paper
Language
en
Open access
closed
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › semigroups and automata theory
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords
weighted finite-state transducers, rational design, finite automata

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