Description: Matlab中有关HMM的函数集。给出了详细的源代码，可以用于hMM在各个领域的研究和开发-Matlab the HMM Function Set. Given the detailed source code, hMM can be used in various fields of research and development Platform: |
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Author:蒋荣 |
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Description: Hidden Markov Toolkit (HTK) 3.2.1
HTK is a toolkit for use in research into automatic speech recognition and has been developed by the Speech Vision Robotics Group at the Cambridge University Engineering Department (http://svr-www.eng.cam.ac.uk) and Entropic Ltd (http://www.entropic.com).-Hidden Markov Toolkit (HTK) 3.2.1 HTK is a t OOLKIT for use in research into automatic speec h recognition and has been developed by the Spee ch Vision Robotics Group at the Cambridge Korea adequate Engineering Department (http :// SVR- www.eng.cam.ac.uk) and Entropic Ltd. (h Miller :// www.entropic.com). Platform: |
Size: 2229248 |
Author:mesu |
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Description: 这个是隐markov模型中viterbi算法实现的一个具体实例程序 很好-This is a hidden markov models Viterbi Algorithm in a specific example of good procedures Platform: |
Size: 1024 |
Author:juliamie |
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Description: 实现了隐马尔可夫模型的算法。可用向前算法和向后算法对模型进行训练。-Realize the hidden Markov model algorithms. Algorithm can be used forward and backward algorithm for training the model. Platform: |
Size: 3603456 |
Author:waterlily |
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Description: 说话人识别方法及其系统的应用开发研究.毕业设计论文,详细.本文对说话人识别方法应用作了较深入系统的研究。采用的方法分别是矢量量化(VQ)识别方法、隐马尔可夫模型(HMM)识别方法、高斯混合模型(GMM)识别方法。-Speaker Recognition Method and system development and research. Graduate design thesis in detail. In this paper, methods of application of speaker recognition system were made more in-depth research. Methods used are vector quantization (VQ) identification methods, hidden Markov model (HMM) to identify methods, Gaussian mixture model (GMM) identification methods. Platform: |
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Author:叶小勇 |
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Description: speech recognition using hidden markov model making it speaker independent
Using Matlab 7.5[2008b] Platform: |
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Author:kani |
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Description: Hidden SemiMarkov models 主要是改进HMM存在状态保持时间的局限性，引入了持续时间的函数显式表达-Hidden SemiMarkov models was mainly to improve HMM state of existence to maintain the limitations of time, the introduction of an explicit function of the duration of expression of Platform: |
Size: 17408 |
Author:Shannon Liu |
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Description: 隐马尔可夫模型工具箱，包括了与HMM相关的所有程序。-Hidden Markov Model toolkit, including all the procedures related with the HMM. Platform: |
Size: 746496 |
Author:龚成 |
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Description: This paper presents a hybrid framework of feature extraction and hidden Markov modeling (HMM) for two-dimensional pattern recognition. Importantly, we explore a new discriminative training criterion to assure model compactness and discriminability. This criterion is derived from the hypothesis test theory via maximizing the confidence of accepting the hypothesis that observations are from target HMM states rather than competing HMM states. Accordingly, we develop the maximum confidence hidden Markov modeling (MC-HMM) for face recognition. Platform: |
Size: 52224 |
Author:rupesh |
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Description: 针对合成孔径雷达(SAR) 图像含有大量斑点噪声的特点,基于Contourlet 的多尺度、局部化、方向性和各向
异性等优点,并结合隐马尔科夫树( HMT) 模型和隐马尔科夫场(MRF) ,提出了一种基于Contourlet 域持续性和聚
集性的SAR 图像模糊融合分割算法。该算法有效捕获了Contourlet 子带的持续性和聚集性,并分别用HMT 和
MRF 来刻画,再依据模糊测度,将多尺度HMT 和MRF 有机融合,建立Contourlet 域HMT2MRF 融合模型,并导
出新模型下的最大后验概率(MAP) 分割公式。对实测SAR 图像进行了仿真,仿真结果和分析表明:与小波域上的
HMT2MRF 融合分割及Contourlet 域上HMT 和MRF 分割算法相比,该算法在抑制斑点噪声的同时,有效地提高
了SAR 图像的分割精度- In view of the speckle noise in the synthetic aperture radar (SAR) images , and based on the Contourlet′s
advantages of multiscale , localization , directionality , and anisot ropy , a new SAR image fusion segmentation
algorithm based on the pe rsis tence and clustering in the Contourlet domain is p roposed. The algorithm captures the
pe rsis tence and clus tering of the Contourlet t ransform , which is modeled by hidden Markov t ree (HMT) and Markov
random field (MRF) , respectively. Then , these two models are fused by fuzzy logic , resulting in a Contourlet
domain HMT2MRF fusion model . Finally , the maximum a poste rior (MAP) segmentation equation for the new fusion
model is deduced. The algorithm is used to emulate the real SAR images . Simulation results and analysis indicate that
the p roposed algorithm effectively reduces the influence of multiplicative speckle noise , imp roves the segmentation
accuracy and p rovides a bet te r visual quality for SAR images ove r the Platform: |
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Author:周二牛 |
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Description: 这是张德丰《MATLAB概率与数理统计分析》随书源码(M文件)。代码包括的内容有：概率分布计算及统计特征、数字特征计算、统计图绘制(盒状图、散度图等)、点估计和区间估计、假设检验、方差分析、曲线拟合、回归分析、因素分析、聚类分析、正交实验设计分析、多元方差分析、判别分析、隐马尔可夫模型建模与参数估计和在语音识别中的应用。-This is Zhang Defeng " MATLAB Analysis of probability and mathematical statistics," with the book source (M file). The contents of the code include: calculation of the probability distribution and statistical characteristics, digital feature calculation, statistical mapping (box-shaped graph, scatter graph, etc.), point and interval estimation, hypothesis testing, analysis of variance, curve fitting, regression analysis, factor analysis, cluster analysis, orthogonal experimental design, multivariate analysis of variance, discriminant analysis, hidden Markov modeling and parameter estimation and application in speech recognition. Platform: |
Size: 32768 |
Author:吴 |
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Description: 介绍了MATLAB 环境下的语音识别系统, 阐述了具体的实现过程。采用离散隐马尔科夫模型, 为提高识别率采用男女2 套参数, 对离散隐马尔科夫模型在实际语音识别系统中遇到的问题进行分析, 并给出相应的解决办法-】A digital speech recognition system and its realization course are introduced in the MATLAB environment.
The DHMM(Discrete Hidden Markov Model) and two sets of parameters for male and female are used to improve
the recognition accuracy. Some problems when DHMM is used in speech recognition system are analyzed and the
corresponding solutions are provided. Platform: |
Size: 150528 |
Author:江丰安迪 |
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Description: The speech signal for the particular isolated word can be viewed as the one generated using the sequential generating probabilistic model known as hidden Markov
model (HMM). Consider there are n states in the HMM. The particular isolated
speech signal is divided into finite number of frames. Every frame of the speech
signal is assumed to be generated from any one of the n states. Each state is modeled as the multivariate Gaussian density function with the specified mean vector
and the covariance matrix. Let the speech segment for the particular isolated word
is represented as vector S. The vector S is divided into finite number of frames
(say M). The i th frame is represented as Si . Every frame is generated by any of the n
states with the specified probability computed using the corresponding multivariate
Gaussian density model. Platform: |
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Author:Khan17
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Description: hidden markov modelling for studing of reliability and availability in power system in different condiktion. Platform: |
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Author:DARKMAN
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