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to model the probabilities of transitions between different states of encoded speech representations. They are often used along with other tools such
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Liu, X. L.; Liang, Y.; Lou, Y. H.; Li, H.; Shan, B. S. (2010), "Noise-Robust Voice
Activity Detector Based on Hidden Semi-Markov Models",
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Shun-Zheng Yu, "Hidden Semi-Markov Models: Theory, Algorithms and
Applications", 1st Edition, 208 pages, Publisher: Elsevier, Nov. 2015
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Statistical inference for hidden semi-Markov models is more difficult than in hidden Markov models, since algorithms like the
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modelled daily rainfall using a hidden semi-Markov model. If the underlying process (e.g. weather system) does not have a
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Bulla, J.; Bulla, I.; Nenadiç, O. (2010), "hsmm – an R Package for
Analyzing Hidden Semi-Markov Models",
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Sansom, J.; Thomson, P. J. (2001), "Fitting hidden semi-Markov models to breakpoint rainfall data",
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Hidden semi-Markov models can be used in implementations of statistical parametric
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Tokuda, Keiichi; Hashimoto, Kei; Oura, Keiichiro; Nankaku, Yoshihiko (2016),
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are not directly applicable, and must be adapted requiring more resources.
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Barbu, V.; Limnios, N. (2008). "Hidden Semi-Markov Model and
Estimation".
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Semi-Markov Chains and Hidden Semi-Markov Models toward
Applications
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392:"Estimating hidden semi-Markov chains from discrete sequences"
78:(HSMM) is a statistical model with the same structure as a
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262:. Lecture Notes in Statistics. Vol. 191. p. 1.
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Yu, Shun-Zheng (2010), "Hidden Semi-Markov Models",
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may be too technical for most readers to understand
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101:duration, an HSMM may be more appropriate.
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62:Learn how and when to remove this message
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110:artificial neural networks
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99:geometrically distributed
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119:and Ted Petrie in 1966.
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