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On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models

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On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models

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Sánchez Peiró, JA.; Rocha, MA.; Romero, V.; Villegas, M. (2018). On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models. Computational Linguistics. 44(1):17-37. https://doi.org/10.1162/COLI_a_00306

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Título: On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models
Autor: Sánchez Peiró, Joan Andreu Rocha, M. A. Romero, Verónica Villegas, Mauricio
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[EN] Probabilistic finite-state automata are a formalism that is widely used in many problems of automatic speech recognition and natural language processing. Probabilistic finite-state automata are closely related to other ...[+]
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
Computational Linguistics. (issn: 0891-2017 )
DOI: 10.1162/COLI_a_00306
Editorial:
MIT Press
Versión del editor: https://doi.org/10.1162/COLI_a_00306
Código del Proyecto:
info:eu-repo/grantAgreement/EC/H2020/674943/EU/Recognition and Enrichment of Archival Documents/
info:eu-repo/grantAgreement/MINECO//TIN2012-37475-C02-01/ES/SEARCH IN TRANSCRIBED MANUSCRIPTS AND DOCUMENT AUGMENTATION/
info:eu-repo/grantAgreement/EC/FP7/600707/EU/tranScriptorium/
Agradecimientos:
This work has been partially supported through the European Union's H2020 grant READ (Recognition and Enrichment of Archival Documents) (Ref: 674943) and the MINECO/FEDER-UE project TIN2015-70924-C2-1-R. The second author ...[+]
Tipo: Artículo

References

Abney, S., McAllester, D., & Pereira, F. (1999). Relating probabilistic grammars and automata. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics -. doi:10.3115/1034678.1034759

Bakis, R. (1976). Continuous speech recognition via centisecond acoustic states. The Journal of the Acoustical Society of America, 59(S1), S97-S97. doi:10.1121/1.2003011

Can, D., & Saraclar, M. (2011). Lattice Indexing for Spoken Term Detection. IEEE Transactions on Audio, Speech, and Language Processing, 19(8), 2338-2347. doi:10.1109/tasl.2011.2134087 [+]
Abney, S., McAllester, D., & Pereira, F. (1999). Relating probabilistic grammars and automata. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics -. doi:10.3115/1034678.1034759

Bakis, R. (1976). Continuous speech recognition via centisecond acoustic states. The Journal of the Acoustical Society of America, 59(S1), S97-S97. doi:10.1121/1.2003011

Can, D., & Saraclar, M. (2011). Lattice Indexing for Spoken Term Detection. IEEE Transactions on Audio, Speech, and Language Processing, 19(8), 2338-2347. doi:10.1109/tasl.2011.2134087

Chi, Z. 1999. Statistical properties of probabilistic context-free grammar. Computational Linguistics, 25(1):131–160.

Corazza, A., & Satta, G. (2007). Probabilistic Context-Free Grammars Estimated from Infinite Distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(8), 1379-1393. doi:10.1109/tpami.2007.1065

Dupont, P., Denis, F., & Esposito, Y. (2005). Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms. Pattern Recognition, 38(9), 1349-1371. doi:10.1016/j.patcog.2004.03.020

Hernando, D., Crespi, V., & Cybenko, G. (2005). Efficient Computation of the Hidden Markov Model Entropy for a Given Observation Sequence. IEEE Transactions on Information Theory, 51(7), 2681-2685. doi:10.1109/tit.2005.850223

Huber, M. F., T. Bailey, H. Durrant-Whyte, and U. D. Hanebeck. 2008. On entropy approximation for Gaussian mixture random vectors. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pages 181–188, Seoul.

Ilic, V. M. 2011. Entropy semiring forward-backward algorithm for HMM entropy computation. CoRR., abs/1108.0347.

Kemp, T. and T. Schaaf. 1997. Estimating confidence using word lattices. Eurospeech, pages 827–830, Rhodes.

Mann, G. S. and A. McCallum. 2007. Efficient computation of entropy gradient for semi-supervised conditional random fields. In Proceedings of HLT-NAACL, Companion Volume, Short Papers, pages 109–112.

Mohri, M., Pereira, F., & Riley, M. (2002). Weighted finite-state transducers in speech recognition. Computer Speech & Language, 16(1), 69-88. doi:10.1006/csla.2001.0184

Nederhof, M.-J., & Satta, G. (2008). Computation of distances for regular and context-free probabilistic languages. Theoretical Computer Science, 395(2-3), 235-254. doi:10.1016/j.tcs.2008.01.010

Puigcerver, J., A. H. Toselli, and E. Vidal. 2014. Word-graph and character-lattice combination for KWS in handwritten documents. In International Conference on Frontiers in Handwriting Recognition (ICFHR), pages 181–186, Crete.

Sanchis, A., A. Juan, and E. Vidal. 2012. A word-based naïve Bayes classifier for confidence estimation in speech recognition. IEEE Transactions on Audio, Speech, and Language Processing, 20(2):565–574.

Soule, S. (1974). Entropies of probabilistic grammars. Information and Control, 25(1), 57-74. doi:10.1016/s0019-9958(74)90799-2

Thompson, R. A. (1974). Determination of Probabilistic Grammars for Functionally Specified Probability-Measure Languages. IEEE Transactions on Computers, C-23(6), 603-614. doi:10.1109/t-c.1974.224001

Tomita, M. 1986. An efficient word lattice parsing algorithm for continuous speech recognition. In Proceedings of ICASSP, pages 1569–1572, Tokyo.

Ueffing, N., F. J. Och, and H. Ney. 2002. Generation of word graphs in statistical machine translation. In Proceedings on Empirical Method for Natural Language Processing, pages 156–163, Philadelphia, PA.

Vidal, E., Thollard, F., de la Higuera, C., Casacuberta, F., & Carrasco, R. C. (2005). Probabilistic finite-state machines - part I. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(7), 1013-1025. doi:10.1109/tpami.2005.147

Wessel, F., Schluter, R., Macherey, K., & Ney, H. (2001). Confidence measures for large vocabulary continuous speech recognition. IEEE Transactions on Speech and Audio Processing, 9(3), 288-298. doi:10.1109/89.906002

Wetherell, C. S. (1980). Probabilistic Languages: A Review and Some Open Questions. ACM Computing Surveys, 12(4), 361-379. doi:10.1145/356827.356829

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