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The thing is, we are not running out of domains.
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Then, the effect of out-of-domain crosstalk is analyzed.
Table 2 presents different training scenarios, combining in-domain with out-of-domain French data.
This was tested on several out-of-domain data resources matching the target language (French).
Hence, out-of-domain knowledge from other targets is mainly beneficial when not enough in-domain knowledge is available.
Hence, many recent studies addressed the exploitation of foreign (i.e., out-of-domain or out-of-language) data for the training of ASR systems [2 4].
Hence, instead of assigning compounds as in- or out-of-domain, they should be associated with different prediction confidences.
Furthermore, we observe that out-of-domain data significantly decreases the ASR performance for the HMM/GMM system.
Note that text segments that did not contain any key phrases were automatically classified as out-of-domain data.
This procedure should avoid overfitting the DNN to the out-of-domain data and generalizes to the target data.
Finally, the results show that our emphasis on clustering features is crucial to develop robust out-of-domain models.
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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