Learning Hidden Markov Models with Hidden Markov Trees as Observation Distributions
Diego Humberto Milone, Leandro Ezequiel Di Persia · INTELIGENCIA ARTIFICIAL · 2008
"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"