Markov models (Markov Model) is a statistical model, widely used in speech recognition, speech automatic annotation, audio and character conversion, the probability of grammar and other natural language processing and other applications. After long-term development, especially in the successful application of speech recognition, making it a common statistical tool.

HMMall\HMM\dhmm_em.m
......\...\dhmm_em_online.m
......\...\dhmm_logprob.m
......\...\dhmm_logprob_brute_force.m
......\...\dhmm_logprob_path.m
......\...\dhmm_sample.m
......\...\dhmm_sample_endstate.m
......\...\fixed_lag_smoother.m
......\...\fwdback.m
......\...\fwdback_xi.m
......\...\fwdprop_backsample.m
......\...\gausshmm_train_observed.m
......\...\mc_sample.m
......\...\mc_sample_endstate.m
......\...\mdp_sample.m
......\...\mhmmParzen_train_observed.m
......\...\mhmm_em.m
......\...\mhmm_logprob.m
......\...\mhmm_sample.m
......\...\mk_leftright_transmat.m
......\...\mk_rightleft_transmat.m
......\...\multinomial_prob.m
......\...\pomdp_sample.m
......\...\transmat_train_observed.m
......\...\viterbi_path.m
......\...\例子\dhmm_em_demo.m
......\...\....\dhmm_em_online_demo.m
......\...\....\fixed_lag_smoother_demo.m
......\...\....\mhmm_em_demo.m
......\...\....\testHMM.m
......\...\....\好例子.m
......\...\无用\publishHMM.m
......\How to use the HMM toolbox.txt
......\KPMstats\#histCmpChi2.m#
......\........\beta_sample.m
......\........\chisquared_histo.m
......\........\chisquared_prob.m
......\........\chisquared_readme.txt
......\........\chisquared_table.m
......\........\clg_Mstep.m
......\........\clg_Mstep_simple.m
......\........\clg_prob.m
......\........\condGaussToJoint.m
......\........\condgaussTrainObserved.m
......\........\condgauss_sample.m
......\........\cond_indep_fisher_z.m
......\........\convertBinaryLabels.m
......\........\cwr_demo.m
......\........\cwr_em.m
......\........\cwr_predict.m
......\........\cwr_prob.m
......\........\cwr_readme.txt
......\........\cwr_test.m
......\........\dirichletpdf.m
......\........\dirichletrnd.m
......\........\dirichlet_sample.m
......\........\distchck.m
......\........\eigdec.m
......\........\est_transmat.m
......\........\fit_paritioned_model_testfn.m
......\........\fit_partitioned_model.m
......\........\gamma_sample.m
......\........\gaussian_prob.m
......\........\gaussian_sample.m
......\........\histCmpChi2.m
......\........\histCmpChi2.m~
......\........\KLgauss.m
......\........\linear_regression.m
......\........\logist2.m
......\........\logist2Apply.m
......\........\logist2ApplyRegularized.m
......\........\logist2Fit.m
......\........\logist2FitRegularized.m
......\........\logistK.m
......\........\logistK_eval.m
......\........\marginalize_gaussian.m
......\........\matrix_normal_pdf.m
......\........\matrix_T_pdf.m
......\........\mc_stat_distrib.m
......\........\mixgauss_classifier_apply.m
......\........\mixgauss_classifier_train.m
......\........\mixgauss_em.m
......\........\mixgauss_init.m
......\........\mixgauss_Mstep.m
......\........\mixgauss_prob.m
......\........\mixgauss_prob_test.m
......\........\mixgauss_sample.m
......\........\mkPolyFvec.m
......\........\mk_unit_norm.m
......\........\multinomial_prob.m
......\........\multinomial_sample.m
......\........\multipdf.m
......\........\multirnd.m
......\........\normal_coef.m
......\........\partial_corr_coef.m
......\........\parzen.m
......\........\parzenC.c
......\........\parzenC.dll
......\........\parzenC.mexglx
......\........\parzenC_test.m

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