• 天天向上学程序
    了解作者
  • matlab
    开发工具
  • 16.2MB
    文件大小
  • rar
    文件格式
  • 0
    收藏次数
  • 1 积分
    下载积分
  • 4
    下载次数
  • 2020-03-30 09:41
    上传日期
利用MFCC特征,采用HMM模式,实现中文数据库的情感识别。
MFCC+HMM.rar
  • MFCC+HMM
  • HMM
  • models
  • training_list.mat.mat
    455B
  • EM_models_S6_struct0.mat
    455B
  • wav
  • a11-wyn.wav
    469.5KB
  • i23_ld2.wav
    288.7KB
  • a42-ld1.wav
    216.7KB
  • u22ld1.wav
    350.6KB
  • e32-xpy.wav
    445.7KB
  • v43-cjq.wav
    310.8KB
  • e33-xpy.wav
    437.3KB
  • o41-rgf.wav
    357.9KB
  • u32-ld2.wav
    443.4KB
  • a43_ld2.wav
    239.3KB
  • o11-ld1.wav
    435KB
  • a22-wyn.wav
    436.7KB
  • u32-zl.wav
    359.7KB
  • a32_ld2.wav
    445.1KB
  • e43-cjq.wav
    253.1KB
  • e31-zl.wav
    338.9KB
  • v13-wyn.wav
    569.3KB
  • v31ld1.txt
    488.3KB
  • v41-xpy.txt
    238.8KB
  • a41-ld1.wav
    221.8KB
  • a22-scy.wav
    270KB
  • e43-wyn.wav
    385.3KB
  • e23-xpy.wav
    382.7KB
  • e42-zl.wav
    208.8KB
  • a31-xpy.wav
    474KB
  • v12-rgf.wav
    776KB
  • a42_ld2.wav
    218.5KB
  • e42ld1.wav
    257.9KB
  • v21-wyn.wav
    454.9KB
  • o32-wyn.wav
    514.9KB
  • i42-cjq.wav
    282.2KB
  • o23-xpy.wav
    393.8KB
  • u43-rgf.wav
    350.4KB
  • a41-scy.wav
    192.5KB
  • e41-scy.wav
    199.1KB
  • u42-zl.wav
    181.7KB
  • e32-scy.wav
    398KB
  • a42-xpy.wav
    191.2KB
  • i23ld1.wav
    371.4KB
  • u41-wyn.wav
    371.8KB
  • a33-cjq.wav
    501.8KB
  • i21-wyn.wav
    333.3KB
  • a41_ld2.wav
    220.1KB
  • a21-xpy.wav
    328.3KB
  • v23-ld2.txt
    286.5KB
  • a33-wyn.wav
    417.5KB
  • v12-scy.wav
    319.5KB
  • o32-scy.wav
    487.2KB
  • o43-rgf.wav
    364.4KB
  • e31-wyn.wav
    500KB
  • i33-xpy.wav
    538.5KB
  • v31-scy.txt
    514.9KB
  • a32-wyn.wav
    430.4KB
  • v11-zl.txt
    356.9KB
  • u42-wyn.wav
    427KB
  • a43-cjq.wav
    224.6KB
  • u11-xpy.wav
    534.3KB
  • a33-xpy.wav
    414.5KB
  • v12-wyn.wav
    552.3KB
  • o11-wyn.wav
    492.1KB
  • a21-cjq.wav
    260.3KB
  • a42-scy.wav
    186KB
  • i11-wyn.wav
    522.8KB
  • i42ld1.wav
    207.9KB
  • i31-rgf.wav
    684.6KB
  • i22_ld2.wav
    254KB
  • a21-scy.wav
    288.6KB
  • e13-cjq.wav
    602.1KB
  • a23-zl.wav
    252.7KB
  • u13-scy.wav
    187.3KB
  • v33-rgf.txt
    678.4KB
  • a32-scy.wav
    405KB
  • v31-xpy.wav
    468.4KB
  • i41-scy.wav
    199.5KB
  • a41-xpy.wav
    166.6KB
  • i41-cjq.wav
    305KB
  • e32-wyn.wav
    479.7KB
  • i33-wyn.wav
    463.6KB
  • e32-zl.wav
    369.7KB
  • e22-zl.wav
    312.4KB
  • a33_ld2.wav
    413.9KB
  • o32-zl.wav
    365.1KB
  • i21-rgf.wav
    341.9KB
  • v23-wyn.txt
    577.3KB
  • a32-cjq.wav
    474.5KB
  • e13-rgf.wav
    787.8KB
  • v21-ld2.wav
    267.6KB
  • u43ld1.wav
    272.3KB
  • e33-rgf.wav
    664.1KB
  • i11-rgf.wav
    910.8KB
  • o23-rgf.wav
    454.3KB
  • u11-wyn.wav
    493.8KB
  • a22-zl.wav
    253.1KB
  • e22-wyn.wav
    411.9KB
内容介绍
This readme document is for version 1.03. The EM training function is updated in this version. Those who are interested in a more easily used version are invited to download version 1.01, in which the structure of HMMs is left-to-right without skips. Those who are interested in high-order hidden Markov models (HO-HMM) or hidden semi-Markov models (HSMM) are invited to visit https://sourceforge.net/projects/ho-hmm/. In this version, the HMMs are allowed to have state-skipping transitions. State 1 and State N in this version are the null start and end state, respectively. The entry point for this package is "main_train_test_EM.m". In that script file, you may need to modify several parameters for the recognition system such as MODEL_NO, dim(the dimension of feature vector), ITERATION_END (which is used to determine the number of training iterations), the range for EMIT_STATE_NO, and the model structure, which is defined by the initialization probabilities, A0, Aij, and Af. A0 is a row vector for the transition probability from the dummy start state to the emitting states, i.e., A0(k) is used to initialize A(1,k+1) Aij is a row vector for the transition probability from an emit-state to itself and to the following states, i.e., Aij(k) is used initialize A(i,i+k-1) for all i. Af is a row vector used to set the transition probability from the last k-th emit-state to the null end state. For each k, if Af(k) is larger than A(N-k,N), then Af(k) is used to replace A(N-k,N) and the probability associated with the transition arcs leaving State k are renormalized. If Af(k) does not exists or Af(k) is not larger than A(N-k,N), then A(N-k,N) will not been affected. Before you start to use the programs, you should first prepare the training and testing data. Excerpts of TIDIGITS database can be obtained from http://cronos.rutgers.edu/~lrr/speech%20recognition%20course/databases/isolated_digits_ti_train_endpt.zip and http://cronos.rutgers.edu/~lrr/speech%20recognition%20course/databases/isolated_digits_ti_test_endpt.zip. The root directory for the training data, isolated_digits_ti_train_endpt, and the root directory for test data, isolated_digits_ti_test_endpt, should be placed under the "wav" directory so that we do not need to modify "main_train_test_EM.m" to run that program. To prepare your own data, you can modify the Matlab script file "main_dr_wav2mfcc_e_d_a.m" for extracting the feature vector sequence from your own waveform data. You also need to create a .mat file containing a list of training data and another .mat file containing a list of testing data, where the first field of a record in the list represents the word id (in integer) and the second field is the path of the data file. Example Matlab script files for creating training and testing list files are "generate_selected_TI_isolated_digits_training_list_mat.m" and "generate_selected_TI_isolated_digits_testing_list_mat.m", respectively. The feature file format used in this version is compactable with the HTK format.
评论
    相关推荐
    • MFCC.zip
      MFCC算法实例及源代码实现,用于提取语音特征进行进一步对比
    • mfcc.rar
      通过语音识别对类型进行分类,使用HMM-GMM模型
    • MFCC.zip
      基于MFCC参数和HMM的低空目标声识别方法研究 ,隐马尔科夫研究语音识别检测敌机
    • HMM语音识别.rar
      隐马尔可夫语音识别模型的MATLAB代码
    • 121114136hmm.zip
      hmm语音识别系统的matlab实现,包括所需函数程序。
    • HMM.zip
      HMM的演示源程序,详细解释了HMM的过程,实现了基本的语音识别功能
    • HMM.rar
      语音识别,matlab,cdhmm………………
    • mfcc特征参数.rar
      mfcc特征参数,都是期刊论文,希望对大家有帮助。
    • 语音识别相关资料(DTW HMM MFCC
      语音识别相关资料,详细描述了语音识别的具体细节。是比较好看的资料.用的方法包括hmm。dtw 。mfcc等。是语音识别系统的设计文档
    • matlabcnhelp.rar
      matlab中文帮助很难找的,快速下载