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Our detailed analysis on the performance of both commercial and noncommercial recognition algorithms provide an insight on possible improvements to existing algorithms to better learn facial features in order to improve recognition under non-ideal conditions.
Furthermore, named entity recognizers can be applied to improve recognition of nouns.
However, the use of phonetic contexts, such as triphones, is well known to improve recognition accuracy.
This motivates us to use unlabeled samples to improve recognition performance while developing classifiers.
Also to improve recognition rate of speech recognition system, overestimation parameter is used.
The idea is interesting because it shows a technique that exploits distributed processing to improve recognition performance.
As mentioned in our introduction, we could have adopted other features and/or methods to improve recognition performance.
In order to improve recognition accuracy, an effort has been put into combining front and angle face images.
By analysing the recognition results, the different bands are fused using a feature level fusion scheme to improve recognition accuracy.
Quality measures play an important role in score-level fusion systems and have been used to improve recognition accuracy [18, 24, 25].
In our experimental setup, FDLP feature extraction is applied, which has already been shown to improve recognition of reverberated speech [52].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com