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Ergodicity of hidden Markov models
Mathematics of Control, Signals, and Systems, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Giovanni B. Di Masi, Lukasz Stettner
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Hidden Markov Models in Bioinformatics
Current Bioinformatics, 2007Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks.
Valeria De Fonzo +2 more
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Contextual Hidden Markov Models
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012Multiple works have proposed extensions of HMMs for handling variability. We focus here on the design of HMMs whose probability distribution on sequences depends on additional external variables that we call the context, which may stand for emotion features in speech recognition, physical features in gesture recognition, etc. We show experimentally the
Radenen, Mathieu, Artières, Thierry
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Partially hidden Markov models
IEEE Transactions on Information Theory, 1996Summary: Partially hidden Markov models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression where the hidden variables may be interpreted as ...
Søren Forchhammer, Jorma Rissanen
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On the structure of hidden Markov models
Pattern Recognition Letters, 2004This paper investigates the effect of HMM structure on the performance of HMM-based classifiers. The investigation is based on the framework of graphical models, the diffusion of credits of HMMs and empirical experiments. Although some researchers have focused on determining the number of states, this study shows that the topology has a stronger ...
Karim T. Abou-Moustafa +2 more
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Filtering on hidden Markov models
IEEE Signal Processing Letters, 2000In this letter, we propose a novel approach to adapt the hidden Markov model (HMM) parameters when the original feature vector sequences are transformed by a causal finite impulse response (FIR) filter. Our approach enables us to be free from the requirement of retraining the whole recognition parameters when the feature vectors are changed and makes ...
Nam Soo Kim, Dong Kook Kim
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Linguistic hidden Markov models
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03., 2004In this paper we develop a hidden Markov model (HMM), called the linguistic HMM (LHMM), suitable for processing sequences of fuzzy vectors. A fuzzy vector B is an n-tuple of fuzzy numbers. Since fuzzy numbers are often associated with linguistic terms, such as "small," "medium," etc., a fuzzy vector can also be called a linguistic vector.
Mihail Popescu +2 more
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A Hidden Markov Model for Seismocardiography
IEEE Transactions on Biomedical Engineering, 2017We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate,
Johan Wahlström +6 more
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2019
Hidden Markov models are tractable to capture long-term dependencies but intractable to compute the transition probabilities of higher-order process. We propose a neural hidden Markov models to compute the transition probabilities of higher-order hidden Markov model by a neural network and reduce the cost of computation.
Zuoquan Lin, Jiehu Song
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Hidden Markov models are tractable to capture long-term dependencies but intractable to compute the transition probabilities of higher-order process. We propose a neural hidden Markov models to compute the transition probabilities of higher-order hidden Markov model by a neural network and reduce the cost of computation.
Zuoquan Lin, Jiehu Song
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Hidden Markov Model with Markovian emission
Monte Carlo Methods and Applications, 2020Abstract In our paper [A. Nasroallah and K. Elkimakh, HMM with emission process resulting from a special combination of independent Markovian emissions, Monte Carlo Methods Appl. 23 2017, 4, 287–306] we have studied, in a first scenario, the three fundamental hidden Markov problems assuming that, given the hidden process, the observed ...
Karima Elkimakh, Abdelaziz Nasroallah
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