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HMM
  • Classification:Numerical Algorithm-Artificial Intelligence - Speech-Voice recognition/combine
  • Development Tool:matlab
  • Sise:37.0 KB
  • Upload time:2012/7/12 3:28:26
  • Uploader:briohu
  • Download Statistics:
Description
Implement a Markov model and yin, are a few examples, additional files, HMM speech recognition and can be used to complete source code can be used directly.




File list:
HMM
..\Examples
..\........\fixed_lag_demo.m
..\........\learn_dhmm_demo.m
..\........\learn_mhmm_demo.m
..\........\online_em_demo.m
..\Old
..\...\example1.m
..\...\fixed_lag_smoother.m
..\...\learn_hmm.m
..\...\online_em.m
..\...\online_em_hmm_demo.m
..\...\online_em_pomdp_demo.m
..\...\sample_markov_chain.m
..\approxeq.m
..\consist.m
..\dist2.m
..\em_converged.m
..\enumerate_loglik.m
..\fhmm_infer.m
..\fixed_lag_demo.m
..\fixed_lag_smoother.m
..\forwards.m
..\forwards_backwards.m
..\gaussian_prob.m
..\gmm.m
..\gmminit.m
..\init_mhmm.m
..\kmeans.m
..\learn_dhmm_demo.m
..\learn_hmm.m
..\learn_mhmm.m
..\learn_mhmm_demo.m
..\mk_dhmm_obs_lik.m
..\mk_fhmm_topology.m
..\mk_ghmm_obs_lik.m
..\mk_mhmm_obs_lik.m
..\mk_stochastic.m
..\normalise.m
..\online_em.m
..\online_em_demo.m
..\prob_path.m
..\README
..\sample_dhmm.m
..\sample_discrete.m
..\sample_mc.m
..\sample_mdp.m
..\sample_mhmm.m
..\sample_pomdp.m
..\testfwrite.bin
..\testfwrite.m
..\viterbi_path.m
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[HMM] - : To make the stress variation in robust speech recognition system to achieve better recognition, research based on hidden Markov models (HMM) adaptive technology, proposed a maximum a posteriori probability (MAP) and the method of maximum likelihood regression (MLLR) for the stress of Adaptive variation in speech. Experimental results show that, compared with the base system, effectively improve the recognition rate of the system in two ways. To SD for the initial models of the maximum posterior probability method to identify best 150 samples of training time, up to 90. 4%.
[hmmtrain] - HMM models, using c language, can be used to pattern recognition source code integrity, and can be used directly.
[HMM1] - Speech recognition training programs, simple and practical. Written in c. Full source code using hidden Markov model has been tested and can be used directly.
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