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Full-Text Articles in Signal Processing
Generalized Hidden Filter Markov Models Applied To Speaker Recognition, John M. Colombi
Generalized Hidden Filter Markov Models Applied To Speaker Recognition, John M. Colombi
Theses and Dissertations
Classification of time series has wide Air Force, DoD and commercial interest, from automatic target recognition systems on munitions to recognition of speakers in diverse environments. The ability to effectively model the temporal information contained in a sequence is of paramount importance. Toward this goal, this research develops theoretical extensions to a class of stochastic models and demonstrates their effectiveness on the problem of text-independent (language constrained) speaker recognition. Specifically within the hidden Markov model architecture, additional constraints are implemented which better incorporate observation correlations and context, where standard approaches fail. Two methods of modeling correlations are developed, and their …
Spatio-Temporal Pattern Recognition Using Hidden Markov Models, Kenneth H. Fielding
Spatio-Temporal Pattern Recognition Using Hidden Markov Models, Kenneth H. Fielding
Theses and Dissertations
A new spatio-temporal method for identifying 3D objects found in 2D image sequences is presented. The Hidden Markov Model technique is used as a spatio-temporal classification algorithm to identify 3D objects by the temporal changes in observed shape features. A new information theoretic argument is developed that proves identifying objects based on image sequences can lead to higher classification accuracies than single look methods. A new distance measure is proposed that analyzes the performance of Hidden Markov Models in a multi-class pattern recognition problem. A three class problem identifying moving light display objects provides experimental verification of the sequence processing …