Six topologies of dynamic Bayesian Networks are evaluated for predicting the future user events: (1) Markov Chain of order 1, (2) Hidden Markov Model, (3) autoregressive Hidden Markov Model, (4) factorial Hidden Markov Model, (5) simple hierarchical Hidden Markov Model and (6) tree structured Hidden Markov Model. Goal of the investigation is to evaluate, which of these models has the best fit f...

This paper deals with unsupervised Bayesian classification of multidimensional data. We propose an extension of a previous method of generalized mixture estimation to the correlated sensors case. The method proposed is valid in the independent data case, as well as in the hidden Markov chain or field model case, with known applications in signal processing, particularly speech or image processi...

In this study we evaluate different HMM topologies in terms of recognition of handwritten numeral strings by considering the framework of the Level Building Algorithm (LBA). By including an end-state in a left-to-right HMM structure we observe a significant improvement in the string recognition performance since it provides a better definition of the segmentation cuts by the LBA. In addition, t...

When phone segmentations are known a priori, normalizing the duration of each phone has been shown to be effective in overcoming weaknesses in duration modeling of Hidden Markov Models (HMMs). While we have observed potential relative reductions in word error rate (WER) of up to 34.6% with oracle segmentation information, it has been difficult to achieve significant improvement in WER with segm...

This work addresses the problem of unsupervised mul-tisensor image segmentation. We propose the use of a recent method which estimates parameters of generalized multisensor Hidden Markov Chains. A Hidden Markov Chain is said to be \generalized" when the exact nature of the noise components is not known; we assume however, that each of them belongs to a nite known set of families of distribution...