Learning Hidden Markov Models with Hidden Markov Trees as Observation Distributions

MILONE, D. H.; DI PERSIA, L. E.

Mar del Plata

Simposio; 9th Argentine Symposium on Artificial Intelligence - 36th JAIIO; 2007

SADIO

Hidden Markov models have been found very useful for a wide range of applications in machine learning. The wavelet transform arises as a new tool for signal and image analysis, with a special emphasis on nonlinearities and nonstationarities. Learning models for wavelet coefficients have been mainly based on fixed-length sequences, but many real applications require to model variable-length, very long or real-time sequences. In this paper, we propose a novel learning architecture for sequences analyzed on a short-term basis, but not assuming stationarity within each frame. Long-term dependencies are modeled with a hidden Markov model which, in each internal state, deals with the local dynamics in the wavelet domain using a hidden Markov tree. The training algorithms for all the parameters in the composite model are developed using the expectation-maximization framework. This novel learning architecture can be useful for a wide range of applications.  We detail experiments with real data for speech recognition. In the results, recognition rates were better than the state of the art technologies for this task.