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[
Speech/Voice recognition/combine
]
mean-K-KPCA
DL : 0
通过核 K- 均值聚类的方法对语音帧进行聚类 , 由于聚类的中心能够很好地代表类内的特征, 用中心样本帧取代该类, 减少了核矩阵的维数, 然后再采用稀疏 KPCA方法对核矩阵进行特征提取。-Through the nuclear K-means clustering method for clustering of speech frames, the cluster center can be a good representative of the class characteristics of the sample frame to replace the class with the center, reducing the dimension of the nuclear matrix, and then use Sparse KPCA method for feature extraction of the nuclear matrix.
Date
: 2025-12-29
Size
: 181kb
User
:
piano
[
Speech/Voice recognition/combine
]
Speech Processing Analysis - MATLAB
DL : 0
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.
Date
: 2025-12-29
Size
: 711kb
User
:
Khan17
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