Description: 介绍了一种非常实用的特征提取新方法，针对稀疏核主成分分析方法在特征提取中的不足, 提出了一种基于核K- 均值聚类的稀疏核主成分分析( Sparse KPCA) 的特征提取方法用于说话人识别。-Introduced a very useful new method of feature extraction for Sparse Kernel Principal Component Analysis in Feature Extraction of the lack of a kernel-based K-means clustering of sparse kernel principal component analysis (Sparse KPCA) of the feature extraction methods for speaker recognition. Platform: |
Size: 122880 |
Author:毋桂萍 |
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Description: Speaker Recognition by training GMM models for the speakers in the system. Also tells if there s an impostor in the system. Platform: |
Size: 560128 |
Author:sam |
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Description: 在实时平台上,高斯混合模型(GMM)具有计算有效性和易于实现的优点。最大似然规则中,模型参数不
断更新,但由于爬山特征,任意的原始模型参数估计通常将导致局部最优 遗传算法(GA)适于求解复杂组合优化问
题及非线性函数优化。提出了基于说话人识别的可以解决GMM局部最优问题的GMM/GA新算法,实验结果表明,
提出的GMM/GA新算法比纯粹的GMM算法能获得更优的效果。
- In real-time platform, the Gaussian mixture model (GMM) with the calculation of the effectiveness and easy to realize benefits. Maximum likelihood rule, the model parameters are not
Broken updates, but due to climbing features, any of the original model parameter estimation will usually result in local optimum genetic algorithm (GA) is suitable for solving complex combinatorial optimization question
Title and non-linear function optimization. Proposed speaker recognition based on GMM can solve the problem of local optimal GMM/GA new algorithm, experimental results show that the
Proposed GMM/GA new algorithm than purely GMM algorithm can get better results. Platform: |
Size: 4448256 |
Author:于高 |
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Description: :高斯混合模型(GMM)是一种经典的说话人识别算法，本文在实现其算法的同时，主要模拟了不同噪声环境情况下高斯混合模型
(GMM)的杭嗓声性能，得到了一些有益结论。
-Gaussian mixture model (GMM) is a classic speaker recognition algorithms, this algorithm at the same time in fulfilling its main simulated environmental conditions under different noise Gaussian mixture model
(GMM) of the Hang throat sound performance, and obtained some useful conclusions.
Platform: |
Size: 119808 |
Author:于高 |
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Description: 文章介绍的说话人识别系统，采用能够反映人对语音的感知特性的9(:频率倒谱系数（9(: <+(=>(2/?8(’0+):
81(..-/-(20@，9<88）作为特征参数，同时考虑到特征参数各维分量对于不同说话人的区分程度，采用加权的办法进行矢
量量化。-This paper introduces the speaker recognition system used to reflect the people s perception of voice characteristics 9 (:-Frequency Cepstral Coefficients (9 (: <+(=>( 2 /? 8 ( 0+):
81 (..-/-( 20 @, 9 "88) as the characteristic parameter, taking into account the characteristic parameters for different components of each dimension of the distinction between the speaker level, using weighted approach to vector
Quantization. .
Platform: |
Size: 60416 |
Author:于高 |
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Description: 是清华大学的一位博士后做的关于说话人识别的报告，对于说话人识别的内容总结的很详细。-Tsinghua University, a post-doctoral do the report on Speaker Recognition, Speaker Recognition for the contents of the summary of very detailed. Platform: |
Size: 384000 |
Author:张艺 |
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Description: Speech recognition (also known as automatic speech recognition or computer speech recognition) converts spoken words to text. The term "voice recognition" is sometimes used to refer to speech recognition where the recognition system is trained to a particular speaker - as is the case for most desktop recognition software, hence there is an element of speaker recognition, which attempts to identify the person speaking, to better recognize what is being said. Speech recognition is a broad term which means it can recognize almost anybody s speech - such as a call-centre system designed to recognize many voices. Voice recognition is a system trained to a particular user, where it recognizes their speech based on their unique vocal sound. Platform: |
Size: 32768 |
Author:Devilly |
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Description: Speaker recognition is the process of identifying a person
based on his voice. It is a challenging task to separate
the speaker identity information (who is speaking it) from
the speech content itself (what is being said). Speaker
recognition has several useful applications including biometric
authentication and intuitive human computer interaction. Platform: |
Size: 146432 |
Author:shammy |
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