DocumentCode :
3581281
Title :
Hidden Markov models: An insight
Author :
Mohd Yusoff, Mohd Izhan ; Mohamed, Ibrahim ; Abu Bakar, Mohd Rizam
Author_Institution :
Telekom Malaysia R&D Sdn Bhd., Cyberjaya, Malaysia
fYear :
2014
Firstpage :
259
Lastpage :
264
Abstract :
Hidden Markov models (HMM) is a probabilistic model consisting of variables representing observations, variables that are hidden, the initial state distribution, transition matrix, and parameters for all observation distributions. The said model is commonly used in speech recognition field and it has seen an increase in terms of usage, which include user profiling in mobile communication networks, and energy disaggregation. This paper shows, via numerical example, the computation of HMM´s forward procedure will exceed the precision range of essentially any machine (even in double precision). It also extends the procedure to include Gaussian mixture hidden Markov models (GMHMM), the procedure that can be used as both a generator of observations, and as a model for how a given observation sequence was generated by an appropriate HMM.
Keywords :
Gaussian processes; hidden Markov models; matrix algebra; mixture models; mobile communication; probability; speech recognition; GMHMM; Gaussian mixture hidden Markov models; energy disaggregation; initial state distribution; mobile communication networks; probabilistic model; speech recognition; transition matrix; Data models; Equations; Hidden Markov models; Information technology; Mathematical model; Multimedia communication; Numerical models; Baum-Welch/Expectation Maximization algorithm; Gaussian mixture hidden Markov models (GMHMM); Hidden Markov models (HMM); forward and backward procedures; simulation procedure;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology and Multimedia (ICIMU), 2014 International Conference on
Type :
conf
DOI :
10.1109/ICIMU.2014.7066641
Filename :
7066641
Link To Document :
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