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Automatic Determination Algorithm for the Optimum Number of States in NL-HMnet

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Discovery Science (DS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1967))

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Abstract

Hidden Markov Network (HMnet)[1] is a kind of statisticalfinite-state automatons. Discrete-type HMnet and NL-HMnet[2] can be used as a language model in a speech recognition system. NL-HMnet has no self-loop transition. The proposed algorithms for constructing Discrete-type HMnet and NL-HMnet require the pre-defined total number of states in HMnet. The optimum number of states can be changed by various factors such as kind of task, number of training samples, and so on. It is desirable to automatically determine the optimum number of states for each condition.

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References

  1. J. Takami and S. Sagayama: A Successive State Splitting Algorithm for Efficient Allophone Modeling. Proc. ICASSP’92, Vol. I, pp. 573–576, 1992.

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  2. M. Suzuki and S. Makino: An Automatic Acquisition Method of Statistic Finite-State Automaton for Sentence. Proc. ICASSP’99, Vol. II, pp. 737–740, 1999.

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  3. F. Jelinek and R. L. Mercer, “Interpolated Estimation of Markov Source Parameters from Sparse Data”, Pattern Recognition in Practice, E. S. Gelsema and L. N. Kanal, North-Holland, pp. 381–397, 1980.

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  4. J. Rissanen: Universal Coding, Information, Prediction, and Estimation. IEEE Trans. IT, Vol. 30, No. 4, pp. 629–636, 1984.

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© 2000 Springer-Verlag Berlin Heidelberg

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Suzuki, M., Makino, S. (2000). Automatic Determination Algorithm for the Optimum Number of States in NL-HMnet. In: Arikawa, S., Morishita, S. (eds) Discovery Science. DS 2000. Lecture Notes in Computer Science(), vol 1967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44418-1_36

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  • DOI: https://doi.org/10.1007/3-540-44418-1_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41352-3

  • Online ISBN: 978-3-540-44418-3

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