[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 speech synthesis to model the probabilities of transitions between different states of encoded speech representations.
They are often used along with other tools such 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.