The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable ‘‘noisy version’’. The chain X is a Markov one and the components of Y are independent conditionally on X. Such a model can be extended in two directions: (i) X is a semi-Markov chain and (ii) the distribution of Y conditionally on X is a ‘‘long dependen...

This paper describes the use of combined linear regression and expost MAP methods for average-voice-based speech synthesis system based on HMM. To generate more natural sounding speech using the average-voice-based speech synthesis system when a large amount of training data is available, we apply ex-post MAP estimation after the linear transformation based adaptation. We investigate how the am...

The restoration of a hidden process X from an observed process Y is often performed in the framework of hidden Markov chains (HMC). HMC have been recently generalized to triplet Markov chains (TMC). In the TMC model one introduces a third random chain U and assumes that the triplet T = (X,U, Y ) is a Markov chain (MC). TMC generalize HMC but still enable the development of efficient Bayesian al...

We investigate models for animal feeding behaviour, with the aim of improving understanding of how animals organise their behaviour in the short term. We consider three classes of model: hidden Markov, latent Gaussian and semi-Markov. Each can predict the typical 'clustered' feeding behaviour that is generally observed, however they differ in the extent to which 'memory' of previous behaviour i...