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In this section, we present the ASR results of all 49 systems submitted to the challenge.
This subsection summarizes the characteristics of the SE/feature enhancement (FE) algorithms submitted to the challenge, which correspond to the components in the enhancement part in Fig. 4. Here, rather than listing all the SE/FE components of the submitted systems, we highlight the methods that effectively dealt with reverberation, based on the challenge results that will be detailed later.
These results were not submitted to the challenge organizers officially (but were developed without knowing the ground truth).
For the prediction submitted to the challenge, we had computed protein-specific combinations.
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The best results were submitted to the REVERB challenge workshop, and our results were the best among the challenge participants in the same category, which clarifies the effectiveness of our proposed approach.
The results in the previous section were submitted to the REVERB challenge workshop.
We examined 91 patients with PD submitted to the CO2 challenge test.
These results are provided to allow fair comparison with the other systems submitted to the REVERB challenge that employed the same training data set [47 – 50].
Twenty-seven papers were submitted to the REVERB challenge [17 43], which include 25 systems for the SE task and 49 systems for the ASR task.
The system we submitted to the REVERB challenge combines an SE front-end and an ASR back-end as shown in Fig. 1.
Note that according to Fig. 2, these settings are not optimal for RealData, but for consistency with the system that we submitted to the REVERB challenge, we also used these settings for the other experiments described in this paper.
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