Offline and online identification of hidden semi-Markov models
We present a new signal model for hidden semi-Markov models (HSMMs). Instead of constant transition probabilities used in existing models, we use state-duration-dependant transition probabilities. We show that our modeling approach leads to easy and efficient implementation of parameter identificati...
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| Veröffentlicht in: | IEEE transactions on signal processing 2005-08, Vol.53 (8), p.2658-2663 |
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| container_title | IEEE transactions on signal processing |
| container_volume | 53 |
| creator | Azimi, M. Nasiopoulos, P. Ward, R.K. |
| description | We present a new signal model for hidden semi-Markov models (HSMMs). Instead of constant transition probabilities used in existing models, we use state-duration-dependant transition probabilities. We show that our modeling approach leads to easy and efficient implementation of parameter identification algorithms. Then, we present a variant of the EM algorithm and an adaptive algorithm for parameter identification of HSMMs in the offline and online cases, respectively. |
| doi_str_mv | 10.1109/TSP.2005.850344 |
| format | Article |
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Then, we present a variant of the EM algorithm and an adaptive algorithm for parameter identification of HSMMs in the offline and online cases, respectively.</description><subject>Adaptive algorithm</subject><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Exact sciences and technology</subject><subject>Expectation maximization (EM) algorithm</subject><subject>Hidden Markov models</subject><subject>Information, signal and communications theory</subject><subject>Maximum likelihood estimation</subject><subject>On-line systems</subject><subject>Online</subject><subject>Parameter estimation</subject><subject>Parameter identification</subject><subject>Power engineering and energy</subject><subject>Predictive models</subject><subject>recursive maximum likelihood (RML)</subject><subject>recursive prediction error (RPE)</subject><subject>semi-Markov models</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Speech processing</subject><subject>State estimation</subject><subject>Telecommunications and information theory</subject><subject>Tensile stress</subject><subject>Transition probabilities</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kEtLAzEQgBdRsFbPHrwsgnraNslOXhdBii-oVLCCt5DuJpi6u6mbreC_N3ULBQ-eZpj5Zpj5kuQUoxHGSI7nL88jghAdCYpygL1kgCXgDAFn-zFHNM-o4G-HyVEIS4QwgGSD5HpmbeUak-qmTH3zm7rSNJ2zrtCd803qbfruylhLg6ld9qTbD_-V1r40VThODqyugjnZxmHyenc7nzxk09n94-RmmhW5wF2GeckAAUOSiMKUlEskLV8IjQogFJdAkBXxOmzppsapMdLG1oIAzRnR-TC56veuWv-5NqFTtQuFqSrdGL8OSkhGckEAInn5L0lEdMQZj-D5H3Dp120Tv1CCSZwDxyJC4x4qWh9Ca6xata7W7bfCSG20q6hdbbSrXnucuNiu1aHQlW11U7iwG2MSBCYycmc954wxuzYwAfGVH6RHiIM</recordid><startdate>20050801</startdate><enddate>20050801</enddate><creator>Azimi, M.</creator><creator>Nasiopoulos, P.</creator><creator>Ward, R.K.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE transactions on signal processing, 2005-08, Vol.53 (8), p.2658-2663 |
| issn | 1053-587X 1941-0476 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive algorithm Adaptive algorithms Algorithms Applied sciences Detection, estimation, filtering, equalization, prediction Exact sciences and technology Expectation maximization (EM) algorithm Hidden Markov models Information, signal and communications theory Maximum likelihood estimation On-line systems Online Parameter estimation Parameter identification Power engineering and energy Predictive models recursive maximum likelihood (RML) recursive prediction error (RPE) semi-Markov models Signal and communications theory Signal processing Signal, noise Speech processing State estimation Telecommunications and information theory Tensile stress Transition probabilities |
| title | Offline and online identification of hidden semi-Markov models |
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