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The latter evaluation showed that the overlapping segments accounted for more than 70% of errors produced by every submitted system.
To investigate the relationship between the SE instrumental test results and the actual audible quality, we conducted the listening test described in Section 3.2.2. Figure 8 shows the listening test results of each submitted system.
Our submitted system used T ⊥=3 for all the array set-ups (i.e., irrespective of the number of microphones used) while T ⊤ was set at 40, 30, and 7 for the 1ch, 2ch, and 8ch set-ups, respectively.
By integrating thread-level features into QA matching, our model achieves the best performance of Macro-F1 58.77%, which is 1.6% higher than the best submitted system of the answer selection task in SemEval2015.
In general, each submitted system had all or a subset of the components shown in Fig. 4. The participants in the SE task mainly focused on the development of the enhancement part in Fig. 4, but the ASR task participants focused on both the enhancement and recognition parts.
For the single-channel scenario, the submitted system in [12] showed the best performance amongst all the submitted results which solely used the ASR back-end system based on the hidden Markov model toolkit (HTK) [13] that was provided as baseline system by the REVERB challenge.
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All the submitted systems allow INV and OOV term detection.
Fig. 7 Frequency-weighted Segmental SNR of submitted systems, listed separately by number of microphones.
In Section 4, we provide an overview of the submitted systems and their key components.
Fig. 5 WERs of submitted systems, listed separately by number of microphones and data used for acoustic model training.
Table 1 shows the segmentation error rate (as defined in Eq. 2) for the seven submitted systems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com