Learning Hidden Markov Models with Hidden Markov Trees as Observation Distributions
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| Format: | Artículo científico |
| Language: | en |
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Asociación Española para la Inteligencia Artificial
2008
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| _version_ | 1876436004082024448 |
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| author | Leandro E. Di Persia |
| author_facet | Leandro E. Di Persia |
| contents | Learning Hidden Markov Models with Hidden Markov Trees as Observation Distributions Leandro E. Di Persia Diego H. Milone Ingeniería Wavelets EM Algorithm Sequence Learning Speech Recognition Hidden Markov Trees Hidden Markov models have been found very useful for a wide range of applications in artificial intelligence.The wavelet transform arises as a new tool for signal and image analysis, with a special emphasis on nonlinearitiesand nonstationarities. However, learning models for wavelet coefficients have been mainly basedon fixed-length sequences. We propose a novel learning architecture for sequences analyzed on a short-termbasis, but not assuming stationarity within each frame. Long-term dependencies are modeled with a hiddenMarkov model which, in each internal state, deals with the local dynamics in the wavelet domain using ahidden Markov tree. The training algorithms for all the parameters in the composite model are developedusing the expectation-maximization framework. This novel learning architecture can be useful for a widerange of applications. We detail experiments with real data for speech recognition. In the results, recognitionrates were better than the state of the art technologies for this task 2008 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503702 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.37 Vol.12 |
| format | Artículo científico |
| id | redalyc_92503702 |
| institution | Redalyc |
| language | en |
| publishDate | 2008 |
| publisher | Asociación Española para la Inteligencia Artificial |
| spellingShingle | Learning Hidden Markov Models with Hidden Markov Trees as Observation Distributions Leandro E. Di Persia Ingeniería Wavelets EM Algorithm Sequence Learning Speech Recognition Hidden Markov Trees Learning Hidden Markov Models with Hidden Markov Trees as Observation Distributions Leandro E. Di Persia Diego H. Milone Ingeniería Wavelets EM Algorithm Sequence Learning Speech Recognition Hidden Markov Trees Hidden Markov models have been found very useful for a wide range of applications in artificial intelligence.The wavelet transform arises as a new tool for signal and image analysis, with a special emphasis on nonlinearitiesand nonstationarities. However, learning models for wavelet coefficients have been mainly basedon fixed-length sequences. We propose a novel learning architecture for sequences analyzed on a short-termbasis, but not assuming stationarity within each frame. Long-term dependencies are modeled with a hiddenMarkov model which, in each internal state, deals with the local dynamics in the wavelet domain using ahidden Markov tree. The training algorithms for all the parameters in the composite model are developedusing the expectation-maximization framework. This novel learning architecture can be useful for a widerange of applications. We detail experiments with real data for speech recognition. In the results, recognitionrates were better than the state of the art technologies for this task 2008 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503702 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.37 Vol.12 |
| title | Learning Hidden Markov Models with Hidden Markov Trees as Observation Distributions |
| topic | Ingeniería Wavelets EM Algorithm Sequence Learning Speech Recognition Hidden Markov Trees |
| url | https://www.redalyc.org/articulo.oa?id=92503702 |