This report introduces a new model for event-driven temporal sequence processing: Generalized Hidden Semi-Markov Models (GHSMMs). GHSMMs are an extension of hidden Markov models to continuous time that builds on turning the stochastic process of hidden state traversals into a semi-Markov process. A large variety of probability distributions can be used to specify transition durations. It is sho...

The EM algorithm, e.g. as Baum-Welch reestimation, is an important tool for parameter estimation in discrete-time Hidden Markov Models. We present a direct reestimation of rate constants for applications in which the underlying Markov process is continuous in time. Previous estimation of discrete-time transition probabilities is not necessary.

The EM algorithm, e.g., the Baum–Welch re-estimation, is an important tool for parameter estimation in discrete-time hidden Markov models. We present a direct re-estimation of rate constants for applications in which the underlying Markov process is continuous in time. Previous estimation of discrete-time transition probabilities is not necessary.

We study the problem of parameter estimation in continuous density hidden Markov models (CD-HMMs) for automatic speech recognition (ASR). As in support vector machines, we propose a learning algorithm based on the goal of margin maximization. Unlike earlier work on max-margin Markov networks, our approach is specifically geared to the modeling of real-valued observations (such as acoustic featu...

We study the problem of parameter estimation in continuous density hidden Markov models (CD-HMMs) for automatic speech recognition (ASR). As in support vector machines, we propose a learning algorithm based on the goal of margin maximization. Unlike earlier work on max-margin Markov networks, our approach is specifically geared to the modeling of real-valued observations (such as acoustic featu...

This paper proposes a new kind of hidden Markov model (HMM) based on multi-space probability distribution, and derives a parameter estimation algorithm for the extended HMM. HMMs are widely used statistical models for characterizing sequences of speech spectra, and have been successfully applied to speech recognition systems. HMMs are categorized into discrete HMMs and continuous HMMs, which ca...

In this contribution we introduce speech emotion recognition by use of continuous hidden Markov models. Two methods are propagated and compared throughout the paper. Within the first method a global statistics framework of an utterance is classified by Gaussian mixture models using derived features of the raw pitch and energy contour of the speech signal. A second method introduces increased te...

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random e...

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...