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However, the computational complexity increases with the mixture number too.
In conclusion, setting the mixture number to 5 is enough to guarantee the detecting accuracy.
So we have to increase Gaussian mixture number to fit its actual distribution.
And ANTCC feature provides the same performance as MFCC when the Gaussian mixture number increases.
From the row, a linear relationship between the mixture number and the CPU time is observed.
We have to increase the Gaussian mixture number to fit its actual distribution.
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Table 1 Identification accuracy with different mixture numbers for clean data of Grid and TIMIT datasets.
Table 5 shows the average accuracy of the proposed methods with different mixture numbers over all the noisy corpora.
From the simulation results, we can see that all the methods can give a good performance for the Grid dataset with different Gaussian mixture numbers.
The first row of Table 4 lists the average CPU time of the proposed methods with different mixture numbers over all the 20 noisy corpora.
Certain predicted freeze point results were fairly accurate, whereas others were decidedly inaccurate, as indicated by the freeze point results of optimal mixture numbers two and nine.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com