Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic coding for lossless data compression. A general 2-part coding scheme for given model order but unknown parameters based on PHMM is presented. A f...

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...

Abstract This work attempts to approximate a linear Gaussian system with finite-state hidden Markov model (HMM), which is found useful in dealing challenges designing networked control systems An indirect approach developed, where state-space (SSM) firstly identified for and the SSM then used as an emulator learning HMM. In proposed method, training data HMM are obtained from generated by throu...

The parameters of a Hidden Markov Model (HMM) are transition and emission probabilities‎. ‎Both can be estimated using the Baum-Welch algorithm‎. ‎The process of discovering the sequence of hidden states‎, ‎given the sequence of observations‎, ‎is performed by the Viterbi algorithm‎. ‎In both Baum-Welch and Viterbi algorithms‎, ‎it is assumed that...

We describe an efficient, self-tuning, explicit and adaptive, hidden Markov model with Duration (the ESTEAHMMD algorithm). The standard hidden Markov model (HMM) constrains state occupancy durations to be geometrically distributed, while the standard hidden Markov model with duration (HMMD) addresses this limitation, but at significant computational expense. A standard HMM requires computation ...

This paper addresses the problem of Hidden Markov Models (HMM) training and inference when the training data are composed of feature vectors plus uncertain and imprecise labels. The “soft” labels represent partial knowledge about the possible states at each time step and the “softness” is encoded by belief functions. For the obtained model, called a Partially-Hidden Markov Model (PHMM), the tra...

This paper compares performance of a hidden Markov model (HMM) and a hybrid HMM/ANN model in seismic events modeling. Observation variables are assumed to follow a Poisson distribution. Parameters of the discrete-time two-state models are estimated on the basis of data on seismic events that were recorded in Poland from 1991 to 1995. Then, on the basis of the estimation results, the most likely...

The objective of this tutorial is to introduce basic concepts of a Hidden Markov Model (HMM) as a fusion of more simple models such as a Markov chain and a Gaussian mixture model. 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. Th...

Recently, a statistical speech synthesis system based on the hidden Markov model (HMM) has been proposed. In this system, spectrum, excitation, and duration of human speech are modeled simultaneously by context-dependent HMMs and speech parameter vector sequences are generated from the HMMs themselves. This system defines a speech synthesis problem in a generative model framework and solves it ...

today, there arevarious statistical models for the discrete simulation of the rainfall occurrence/non-occurrence with more emphasizing on long-term climatic statistics. nevertheless, the accuracy of such models or predictions should be improved in short timescale. in the present paper, it is assumed that the rainfall occurrence/non-occurrence sequences follow a two-layer hidden markov model (hm...