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This class library contains more than 20 classes including feature extraction algorithms (MFCC, LPCC) and modeling techniques (HMM, GMM, DTW, VQ ) for automatic speech recognition and speaker verification
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使用voicebox进行说话人识别,初步介绍如何用GMM（Gaussian Mixture Model）的方法来进行说话人辨识（Speaker Identification），并在Matlab下，尝试通过调整参数来提高得分-For speaker recognition using the voicebox, initially describes how to use GMM (Gaussian Mixture Model) approach to speaker recognition (S
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说话人识别和训练系统所用的很多源码，内容很详实，希望大家能用的上-Speaker Recognition and training system used by a lot of source code, content is very informative and hope that we can use the upper
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Speaker Recognition by training GMM models for the speakers in the system. Also tells if there s an impostor in the system.
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这个是用Matlab写成的说话人识别演示程序。-Currently, there are more than 20 classes in this library, including commonly used feature extraction algorithms and modeling techniques for speech recognition and speaker verification.-Currently, there are more than 20 classe
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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 ofte
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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
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本文章描述了说话人识别中GMM模型中的聚类算法的研究-This article describes the GMM Speaker Recognition Model Clustering Algorithm
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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 approxi
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用于说话人识别初始化样本的聚类算法
调试成功-Speaker Recognition for initialization of the clustering algorithm samples
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This paper presents results of speaker recognition experiments using short Polish sentences. We developed and analyzed various vector quantization representations in order to first maximize identification effectiveness and second to compare VQ (vecto
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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 pi
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用于说话人识别（声纹识别）训练过程或识别过程的高斯混合模型-GMM model for training process or testing process of Speaker recognition
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基于GMM的说话人识别，搭建了一个说话人识别系统用于试验测试，验证了一些参数对性能
的影响，同时使用了多线程并行处理技术，以此缩短识别时间:并提出了一种放
大特征向量差距，变换特征向量在特征空间的分布来提升大容量语音库中说话人
识别率的方法。
-GMM-based speaker recognition, to build a speaker recognition system used for pilot testing to verify the performance i
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微软研究院的说话人识别工具包，包括GMM-UBM、I-Vector。其中demo_gmm_ubm_artificial.m和demo_ivector_plda_artificial.m为生成模拟特征参数进行训练与识别的教学示例，十分适合初学者学习说话人识别基础算法。具体使用方法请看内部文档。-Microsoft Research s speaker recognition toolkit, including GMM-UBM, I-Vector. Demo_gmm_ubm_artificial.
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The number of states in GMM as the generative model of the frames is obtained using
k-means algorithm. This also helps to initialize the mean vector and the covariance
matrix of the individual state of the GMM. The training LPC frames collected fro
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