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Description: 计算高斯混合模型先验概率和后验概率的程序，采用大矩阵运算，大大提高了运行速度。-Gaussian mixture model calculated a priori probability and the probability of post-mortem procedures, using a large matrix computation, greatly improved speed.
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Description: 改进的gmm（高斯混合模型）算法，是单一高斯几率密度函数的衍生-Improved gmm (GMM) algorithm, a single Gaussian probability density function of the derivative
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Author: rch |
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Description: Probability distribution functions.
estimation - (dir) Probability distribution estimation.
dsamp - Generates samples from discrete distribution.
erfc2 - Normal cumulative distribution function.
gmmsamp - Generates sample from Gaussian mixture model.
gsamp - Generates sample from Gaussian distribution.
cmeans - C-means (or K-means) clustering algorithm.
mahalan - Computes Mahalanobis distance.
pdfgauss - Computes probability for Gaussian distribution.
pdfgmm - Computes probability for Gaussian mixture model.
sigmoid - Evaluates sigmoid function.-Probability distribution functions.
estimation- (dir) Probability distribution estimation.
dsamp- Generates samples from discrete distribution.
erfc2- Normal cumulative distribution function.
gmmsamp- Generates sample from Gaussian mixture model.
gsamp- Generates sample from Gaussian distribution.
cmeans- C-means (or K-means) clustering algorithm.
mahalan- Computes Mahalanobis distance.
pdfgauss- Computes probability for Gaussian distribution.
pdfgmm- Computes probability for Gaussian mixture model.
sigmoid- Evaluates sigmoid function.
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Author: 林枫 |
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Description: 高斯混合模型[Gaussian mixture model,简称GMM]是单一高斯机率密度函数的延伸,由於GMM 能够平滑地近似任意形状的密度分布,因此近年来常被用在语音与语者辨识,得到不错的效果。 -Gaussian mixture model [Gaussian mixture model, referred to as GMM] are single-Gaussian probability density function of the extension.GMM can approximate arbitrary smooth shape of the density distribution, so it is often used in speech and speaker recognition in recent years.
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Size: 63488 |
Author: 杨清山 |
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Description: 高斯混合模型是單一高斯機率密度函數的延伸，由於GMM 能夠平滑地近似任意形狀的密度分佈，因此近年來常被用在語音與語者辨識，得到不錯的效果。-Gaussian mixture model is a single Gaussian probability density function of the extension, as the GMM can approximate arbitrary smooth shape of the density distribution, it is often used in recent years in speech and speaker recognition, get good results.
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Author: geyu |
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Description: GMM-GMR is a set of Matlab functions to train a Gaussian Mixture Model (GMM) and retrieve generalized data through Gaussian Mixture Regression (GMR). It allows to encode efficiently any dataset in Gaussian Mixture Model (GMM) through the use of an Expectation-Maximization (EM) iterative learning algorithms. By using this model, Gaussian Mixture Regression (GMR) can then be used to retrieve partial output data by specifying the desired inputs. It then acts as a generalization process that computes conditional probability with respect to partially observed data.
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Author: ning |
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Description: GMM高斯混合模型大规模概率对数计算
需要一个模型地址文件和一个需要识别的声音的mfc文件可以一次执行大批量-GMM Gaussian mixture model probability on the number of large-scale computing need a model of address file, and the voice of the mfc file which need to be identified .can be an implementation of a large number of training
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Author: firelord1989 |
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Description: Usually, speaker recognition systems do not take into account the short–term dependence between the vocal source and the vocal tract. A feasibility study that retains this dependence is presented here. A model of joint probability functions of the pitch and the feature vectors is proposed. Three strategies are designed and compared for all female speakers taken from the SPIDRE corpus.
The fi rst operates on all voiced and unvoiced speech segments (baseline strategy). The second strategy considers only the voiced speech segments and the last includes the short–term pitch information along with the standard MFCC. We use two pattern recognizers: LVQ–SLP and GMM. In all cases, we observe an increase in the identifi cation rates and more specifi cally when using a time duration of 500 ms (6 higher).-Usually, speaker recognition systems do not take into account the short–term dependence between the vocal source and the vocal tract. A feasibility study that retains this dependence is presented here. A model of joint probability functions of the pitch and the feature vectors is proposed. Three strategies are designed and compared for all female speakers taken from the SPIDRE corpus.
The fi rst operates on all voiced and unvoiced speech segments (baseline strategy). The second strategy considers only the voiced speech segments and the last includes the short–term pitch information along with the standard MFCC. We use two pattern recognizers: LVQ–SLP and GMM. In all cases, we observe an increase in the identifi cation rates and more specifi cally when using a time duration of 500 ms (6 higher).
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Author: morteza |
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Description: 模式识别中，GMM很多地方用来聚类，也有用GMM进行分类，此代码功能为如何计算阈值，得到后验概率，与阈值做比较。。得到分类结构-GMM classification, calculating a threshold value, posterior probability
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Size: 57344 |
Author: zhong Hongyan |
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Description: 语音处理GMM相关算法,1.计算概率密度并画出高斯混合模型，2.计算边际，条件混合高斯密度,3估计两个GMM模型的Kullback-Leibler divergence。-GMM relating to speech processing algorithms.1,to calculate probability densities from or plot a Gaussian mixture model.2,marginal and conditional Gaussian mixture densities. 3, Kullback-Leibler divergence between two GMMs .
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Author: 王愈 |
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Description: 高斯混合模型GMM的C++实现
在高斯混合模型中需要使用概率更新参数的地方，程序中都简化成为了1处理，否则计算一个正态分布的概率还是挺花时间的。但是除了将概率换成1，其他地方还是严格按照公式的，大家可以仔细推导一下，就会看出其中的差异-
Gaussian mixture model GMM C++ implementation
In the Gaussian mixture model parameters need to use probability to update the place, the program has become a simplified treatment, or calculate the probability of a normal distribution was quite time-consuming. But in addition to the probability replaced by 1 or elsewhere in strict accordance with the formula, we can derive a closer look, you will see the differences
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Author: 何峰 |
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Description: 聚类算法之高斯混合模型，GMM 和 k-means 很像，不过 GMM 是学习出一些概率密度函数来（所以 GMM 除了用在 clustering 上之外，还经常被用于 density estimation ）。-Gaussian mixture model of clustering algorithm, GMM and k-means like, but GMM is learning some probability density function (so GMM except on clustering, but also often used for density estimation).
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Author: 赵小娟 |
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Description: 高斯模型就是用高斯概率密度函数（正态分布曲线）精确地量化事物，将一个事物分解为若干的基于高斯概率密度函数（正态分布曲线）形成的模型。(Gaussian model is to use Gaussian probability density function (normal distribution curve) to accurately quantify things, a thing is divided into several based on the Gaussian probability density function (normal distribution curve) to form the model.)
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Author: Byydyh
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