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The movement was regulated by a selective interaction between the synthesized nanofiber and the surface of the honeycomb frames of the template.
However, since there were 749 new GMMs for labeling the frames of the template representatives, there was an extra memory overhead of 5.4 MB.
While MDTS stops here, MLTS reselects the GMM labels for the representative frames, and template compression generates new GMMs and uses them to relabel the frames of the template representatives.
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Use the six frames on the template to graphically represent the main points from the article.
The load on memory storage is reduced by using Gaussian mixture model (GMM) indices to represent the speech frames of the templates.
The remaining Gaussian components are used to construct a new GMM, and the new GMM is used as one of the l GMMs to label the corresponding frame of the template representative.
In template compression, new GMMs are generated for labeling the frames of the selected template representatives so as to better capture the information in the training Templates.
In Figure 7, we show the effects of using different numbers of GMMs (n = 1, n = 3, n = 5, and n = 7) in labeling each frame of the templates.
We further propose to fine tune the MLTS template representatives by using a GMM merging algorithm so that the GMMs can better represent the frames of the selected template representatives.
The frames of the selected template representatives are subsequently indexed by their n-best GMMs according to Section 3.1.
Therefore, we will group the features of selected shot change frames to build the template of pitching view.
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