《matlab扩展编程》光盘资料.关于端点检测，录音，参数提取，HMM,LPC,MFCC,DYW等一些源代码
上传时间： 2013-12-18
上传用户：hongmo
Wavelet Subband coding for speaker recognition The fn will calculated subband energes as given in the att tech paper of ruhi sarikaya and others. the fn also calculates the DCT part. using this fn and other algo for pattern classification(VQ,GMM) speaker identification could be achived. the progress in extraction is also indicated by progress bar.
标签： recognition calculated Wavelet Subband
上传时间： 2013-12-08
上传用户：guanliya
哈工大博士论文，基于HMM和ANN的汉语语音识别。
标签： 论文
上传时间： 2013-12-29
上传用户：225588
L3_1.m: 純量量化器的設計(程式) L3_2.m: 量化造成的假輪廓(程式) L3_3.m: 向量量化器之碼簿的產生(程式) L3_4.m: 利用LBG訓練三個不同大小與維度的碼簿並分別進行VQ(程式) gau.m: ML量化器設計中分母的計算式(函式) gau1.m: ML量化器設計中分子的計算式(函式) LBG.m: LBG訓練法(函式) quantize.m:高斯機率密度函數的非均勻量化(函式) VQ.m: 向量量化(函式) L3_2.bmp: 影像檔 lena.mat: Matlab的矩陣變數檔
上传时间： 2013-12-26
上传用户：jiahao131
详细介绍了隐马尔科夫链的原理和matlab代码实现，可以运行其中的demo了解hmm的工作原理
上传时间： 2013-12-27
上传用户：love_stanford
隐含马尔可夫模型的入门资料，stanford机器学习课程资料 Introduction to the HMM model.
标签： 马尔可夫模型
上传时间： 2017-09-04
上传用户：huangld
这是一个模型介绍和常用算法的C语言的实现，包过HMM算法，BP神经网络解决异或问题~~
上传时间： 2013-11-25
上传用户：duoshen1989
基于MATLAB的孤立词语音识别系统分析,可以参考一下
标签： 孤立字
上传时间： 2015-03-31
上传用户：王金栋888
隐马尔科夫模型压缩包。。。隐马尔科夫模型的离散形式及连续形式的实现。。。
标签： HMM
上传时间： 2016-03-03
上传用户：dsgadgad
This paper presents a Hidden Markov Model (HMM)-based speech enhancement method, aiming at reducing non-stationary noise from speech signals. The system is based on the assumption that the speech and the noise are additive and uncorrelated. Cepstral features are used to extract statistical information from both the speech and the noise. A-priori statistical information is collected from long training sequences into ergodic hidden Markov models. Given the ergodic models for the speech and the noise, a compensated speech-noise model is created by means of parallel model combination, using a log-normal approximation. During the compensation, the mean of every mixture in the speech and noise model is stored. The stored means are then used in the enhancement process to create the most likely speech and noise power spectral distributions using the forward algorithm combined with mixture probability. The distributions are used to generate a Wiener filter for every observation. The paper includes a performance evaluation of the speech enhancer for stationary as well as non-stationary noise environment.
标签： Telecommunications Processing Signal for
上传时间： 2020-06-01
上传用户：shancjb
