文件名称:HMM-Introdunction

  • 所属分类:
  • 语音合成与识别
  • 资源属性:
  • [Matlab] [源码]
  • 上传时间:
  • 2012-11-26
  • 文件大小:
  • 5.17mb
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  • 0次
  • 提 供 者:
  • 刘*
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关于HMM入门、HMM在语音识别中应用的一些经典资料和Matlab实现代码,包含中文资料-Getting Started on the HMM, HMM in speech recognition applications to achieve some of the classic data and Matlab code that contains the Chinese data
相关搜索: hmm
语音识别
HMM

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下载文件列表

HMM入门及在语音信号处理中的应用

...............................\A tutorial on Hidden Markov Models and selected applications in speech recognition.pdf

...............................\E6820-L10-ASR-seq.pdf

...............................\E6820-L11-ASR-sys.pdf

...............................\hmm-chap.pdf

...............................\HMM-中文课件.pdf

...............................\HMM的学习问题和解码问题研究.nh

matlab代码

..........\HMMall

..........\......\HMM

..........\......\...\#fwdback.m#

..........\......\...\#mhmm_em.m#

..........\......\...\#README.txt#

..........\......\...\dhmm_em.m

..........\......\...\dhmm_em_demo.m

..........\......\...\dhmm_em_online.m

..........\......\...\dhmm_em_online_demo.m

..........\......\...\dhmm_logprob.m

..........\......\...\dhmm_logprob_brute_force.m

..........\......\...\dhmm_logprob_path.m

..........\......\...\dhmm_sample.m

..........\......\...\dhmm_sample_endstate.m

..........\......\...\fixed_lag_smoother.m

..........\......\...\fixed_lag_smoother_demo.m

..........\......\...\fwdback.m

..........\......\...\fwdback.m~

..........\......\...\fwdback_xi.m

..........\......\...\fwdprop_backsample.m

..........\......\...\fwdprop_backsample.m~

..........\......\...\gausshmm_train_observed.m

..........\......\...\herbert.txt~

..........\......\...\mc_sample.m

..........\......\...\mc_sample_endstate.m

..........\......\...\mdp_sample.m

..........\......\...\mhmmParzen_train_observed.m

..........\......\...\mhmm_em.m

..........\......\...\mhmm_em.m~

..........\......\...\mhmm_em_demo.asv

..........\......\...\mhmm_em_demo.m

..........\......\...\mhmm_logprob.m

..........\......\...\mhmm_sample.m

..........\......\...\mk_leftright_transmat.m

..........\......\...\mk_rightleft_transmat.m

..........\......\...\pomdp_sample.m

..........\......\...\publishHMM.m

..........\......\...\README.txt

..........\......\...\README.txt~

..........\......\...\testHMM.m

..........\......\...\transmat_train_observed.m

..........\......\...\viterbi_path.m

..........\......\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_a

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