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We implement this method in a real environment using the optimal compensation parameters estimated from an artificial environment.
We implemented this method in a real environment using the optimal compensation parameters estimated in an artificial environment (α=n=0.5).
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We also devise the model compensation scheme by synthesizing the training data using the optimal regression parameters and by selecting the optimal HMM for the test speech.
Then, stretch using the optimal.
The final SI (FSI) is constructed using motion compensation based on the refined motion vectors and the optimal compensation mode.
Otherwise, output the optimal compensation scheme.
The optimal compensation (beta _0) depends on the difference between u and r.
Additionally, the optimal compensation is non-monotone if the correlation is large.
As above, we analyse the optimal compensation from the creditor's perspective and the resulting filtering failure.
We assume that the optimal compensation parameters for GSS are dependent on the acoustic environment and utterance content.
In this section, we characterize the optimal compensation parameters (beta _0) and (beta _1) from the creditors' perspective.
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