The objective of this tutorial is to introduce basic concepts of a Hidden Markov Model (HMM). The tutorial is intended for the practicing engineer, biologist, linguist or programmer who would like to learn more about the above mentioned fascinating mathematical models and include them into one’s repertoire. This part of the tutorial is devoted to the basic concepts of a Hidden Markov Model. You...

We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data. These three hyperparameters define a hierarchical Dirichlet process capable of capturing a rich set...

We extend the model of Karlof and Wagner for modelling side channel attacks via Input Driven Hidden Markov Models (IDHMM) to the case where not every state corresponds to a single observable symbol. This allows us to examine algorithms where errors in measurements can occur between sub-operations, e.g. there may be an error probability of distinguishing an add (A) versus a double (D) for an ell...

This paper proposes a Bayesian approach to hidden semiMarkov model (HSMM) based speech synthesis. Recently, hidden Markov model (HMM) based speech synthesis based on the Bayesian approach was proposed. The Bayesian approach is a statistical technique for estimating reliable predictive distributions by treating model parameters as random variables. In the Bayesian approach, all processes for con...