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Let W be the test utterance to be recognized.
Figure 10 shows EERs of the parallel frontend system with different test utterance lengths.
Each test utterance was segmented according to the LDC provided transcriptions.
That is models with LDA and models LPP are simultaneously used to score all test utterance.
Figure 10 EERs of parallel frontend systems of different test utterance lengths.
During recognition, our goal is to warp the frequency scale of each test utterance to best match the canonical HMMs,.
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In total, 400 test utterances were recorded.
There are 3233 training utterances and 1206 test utterances, containing 12510 and 4670 words, respectively.
Comparing to the last evaluation, the account of test utterances is rapidly increased.
The trajectories are smooth and are similar to some test utterances.
The test utterances were enhanced using the four schemes, mentioned in Section 4.1.
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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