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Description: 语音识别中经典的HMM算法，包括产生序列、测试和参数训练，由C语言编写。- In speech recognition classical HMM algorithm, including has
the sequence, the test and the parameter training, compiles by the C
language.
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Size: 13312 |
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Description: 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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Size: 1405952 |
Author: 站长 |
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Description: 语音识别中的模型和算法：动态时间归正技术（DTW），隐马尔可夫模型（HMM），高斯混合模型（GMM），高斯混合模型（GMM）-Speech Recognition model and algorithms : dynamic time Reformed technology (DTW) Hidden Markov Model (HMM), Gaussian mixture model (GMM), Gaussian mixture model (GMM)
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Size: 12288 |
Author: 蒋 |
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Description: 利用hmm的孤立字（词）语音识别系统，经典的算法，没有图形界面，需要的可以完善一下。-use hmm isolated words (words) speech recognition system, the classical algorithm, no graphical interface, need to be perfect in this.
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Size: 268288 |
Author: 张维镇 |
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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: 文中详细介绍了一种基于GMM 与 SVM的说话人识别系统。包括特征提取，算法实现及实验数据。对语音处理及说话人识别技术的研究者很有帮助。-Introduce a novel speake identification system based on GMM and SVM,with feature extract,algorithm research and experiment data.This doc would be helpful to those who are working on speech or speaker identification issue.
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Size: 608256 |
Author: 张晓宇 |
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Description: 使用GMM模式建立的语音识别系统，matlab源代码，供大家参考！-GMM model using the speech recognition system, matlab source code for your reference!
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Size: 1024 |
Author: 王良 |
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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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Size: 195584 |
Author: geyu |
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Description: 完整的语音识别程序，训练模型固定。可以运行出识别结果的~-entire speech recognition programme,with the trainning model given.On matlab,we can get the result of recognition.
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Size: 55296 |
Author: 杜敏 |
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Description: Gaussian Mixture Models (GMM) for speech noise reduction
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Size: 258048 |
Author: eddy |
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Description: 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 classes in this library. including commonly used feature extraction al gorithms and modeling techniques for re speech cognition and speaker verification.
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Author: liximin |
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Description: GMM是语音识别中比较成熟的算法之一，这个MATALB完整的实现了基于GMM算法的识别训练程序，可以适用于GMM识别和学习的同行的参考-GMM is a speech recognition algorithm for the more mature one, this MATALB full implementation of the identification algorithm based on GMM training procedures, can be applied to GMM counterparts to identify and study the reference
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Size: 2048 |
Author: yilingzhu |
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Description: 本程序为EMD-HHT-M源代码，供大家交流学习，语音识别专用代码-This program is EMD-HHT-M source code for all to share learning, speech recognition-specific code
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Size: 3756032 |
Author: wrd |
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Description: 这是一个GMM模型的语音识别代码 非常详细 而且实用。-This is a GMM model for speech recognition is very detailed and useful code.
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Size: 35840 |
Author: zhangge |
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Description: This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification.-This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification.
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Author: kvga |
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Description: 基于MFCC的GMM的说话人识别，是很好的语音处理程序-MFCC of the GMM based speaker recognition, speech processing is a very good program
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Author: 周钰川 |
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Description: 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 (vector quantization) and GMM (Gaussian mixture model) approaches. For the research and experiments we created and exploited database, containing specially prepared short speech sequences.
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Size: 258048 |
Author: Tomasz |
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Description: gmm模型下的语音情感识别系统，GMM只是一个数学模型，只是对数据形态的拟和，但是和你所看到的数据分布存在出入也是正常的，因为用EM估计GMM的那些参数时，一般假设我们所得到的数据是不完备的（也就是说假设我们看到的数据分布不是真正的分布，它在运算时把那部分丢失或者叫隐藏的数据“补”上了）-gmm model speech emotion recognition system, GMM is a mathematical model, but fitting the data form, but you can see there is access to the data distribution is normal, because those with the EM estimated GMM parameters, the general assumption we get the data is incomplete (that is, assuming we see the distribution of the data distribution is not real, it is part of the operation at the time that is lost or hidden data is called " fill" on the)
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Size: 11264 |
Author: 张忠顺 |
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Description: 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 from
three speech segments are subjected to PCA for dimensionality reduction and are
subjected to k-means algorithm. The total number of frames is equal to the total
number of vectors that are subjected to k-means clustering.
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Size: 728064 |
Author: Khan17
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Description: 基于MFCC的GMM的语音识别，先是通过MFCC提取语音信号的特征向量，然后用GMM进行分类识别(Voice recognition of GMM based on MFCC.First, the feature vectors of speech signals are extracted through MFCC, and then classified by GMM.)
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Size: 1502208 |
Author: Akiko |
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