Signal recognition models compared for random and Markov presentation sequences

Markov Random Field Models for Pose Estimation in Object Recognition

In this paper, we explore theoretical models for pose estimation and object matching based on Markov random elds (MRFs) and the maximum a posteriori (MAP) probability principle. The set of pose estimates as well as matching estimates are considered to be MRFs whose prior distributions are used as the prior constraints. The MAP solution is found from these distributions and an assumed observatio...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Hidden Markov Models for Labeled Sequences

A hidden Markov model for labeled observations, called a CHMM, is introduced and a maximum likelihood method is developed for estimating the parameters of the model. Instead of training it to model the statistics o f the training sequences it is trained to optimize recognition, It resembles MMI training, but is more general, and has MMI as a special case. The standard forwardbackward procedure ...

Hidden Gauss-Markov models for signal classification

Continuous-state hidden Markov models (CS-HMMs) are developed as a tool for signal classification. Analogs of the Baum, Viterbi, and Baum–Welch algorithms are formulated for this class of models. The CS-HMM algorithms are then specialized to hidden Gauss–Markov models (HGMMs) with linear Gaussian state-transition and output densities. A new Gaussian refactorization lemma is used to show that th...