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We used the backpropagation algorithm with labels obtained by performing an HMM state alignment of the clean speech training data using a GMM-based recognizer trained with the maximum likelihood criterion.
The codes and labels obtained by the ECMT were filtered with these tables.
Using the hydrodynamic radii for the labels obtained by dynamic light scattering (20, 26 and 32 nm), we obtained α = 20° for the angle and x = 1.7 nm.
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A lower value (close to the authentic label) obtained by output of the SVR demonstrates that the test iris and the involved enrolled iris are in the same pattern class.
In the following, we will denote each of the 10 models with a unique label obtained by concatenating the acronyms referring to its characteristics (Table 1).
Given a new instance, all pairwise classifiers are trained to predict its label, and the ranking of the labels is obtained by counting the votes of each label, where if the instance is classified into a label, the label receives one vote.
Lastly, it has been demonstrated that the labels obtained can be detected by measuring the spectral response.
Finally, the output diarization labels are obtained by a Viterbi decoding of so-calculated speaker scores.
The second dataset consists of general English tweets too, but the tweet labels were obtained by manual annotation.
In actual practice, the state labels are obtained by means of a forced alignment using a conventional hidden Markov model (HMM) recognizer.
Hence, a set of multiplexable sphere labels is obtained by using one MLC (acting as the phosphorescent donor and present in constant concentration) and one acceptor dye (which varies in terms of both spectral properties and concentration).
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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