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Fig. 13 Performance of the baseline system.
The first phase was to analyze the performance of the baseline system without user feedback in field tests.
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However, if speaker adaptation techniques are employed, un-transcribed foreign data was not able to improve the performance of the baseline systems trained on target data only.
Table 9 Summary of the performance gains by adaptation of the baseline system to the three emotion-related states M otherese, E mphatic, and A nger.
Experimental results of the proposed HEPLR system on the NIST LRE 2009 evaluation set show better performance than the baseline system.
Table 9 summarises the performance gains obtained by the adaptation of the baseline system to the speech of the three emotion-related states M otherese, E mphatic, and A nger and shows which of the improvements are significant at a level of at least 0.05.
The accuracy and precision of the baseline system was calculated.
The experiments also demonstrated that under mismatched conditions, the relative performance of the SVM baseline system can be considerably better than the GMM-UBM baseline system than for the case of under degraded conditions.
We compare the performance of the proposed system with the baseline system.
At the same time, the performance of our baseline system can be evaluated with respect to other similar systems described in the literature.
The removal of stop-words in combination with the NP chunking system gives a further improvement of performance, but to a much smaller extent than for the baseline system.
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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