Hidden semi-Markov model

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A hidden semi-Markov model (HSMM) is a statistical model with the same structure as a

Markov. This means that the probability of there being a change in the hidden state depends on the amount of time that has elapsed since entry into the current state. This is in contrast to hidden Markov models where there is a constant probability of changing state given survival in the state up to that time.[1]

For instance Sansom & Thomson (2001) modelled daily rainfall using a hidden semi-Markov model.[2] If the underlying process (e.g. weather system) does not have a geometrically distributed duration, an HSMM may be more appropriate.

Hidden semi-Markov models can be used in implementations of statistical parametric

artificial neural networks, connecting with other components of a full parametric speech synthesis system to generate the output waveforms.[3]

The model was first published by Leonard E. Baum and Ted Petrie in 1966.[4][5]

Statistical inference for hidden semi-Markov models is more difficult than in hidden Markov models, since algorithms like the Baum–Welch algorithm are not directly applicable, and must be adapted requiring more resources.

See also

References

  1. S2CID 1899849
    .
  2. S2CID 123113970
    .
  3. ^ Tokuda, Keiichi; Hashimoto, Kei; Oura, Keiichiro; Nankaku, Yoshihiko (2016), "Temporal modeling in neural network based statistical parametric speech synthesis" (PDF), 9th ISCA Speech Synthesis Workshop, 9: 1, archived from the original (PDF) on 2021-03-13
  4. ISBN 978-0-387-73171-1
    .
  5. doi:10.1214/aoms/1177699147
    .

Further reading

External links


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