## 7 Human Assisted Cross-Show Diarization
### Performance and Analyses of Cross-Show Human Assisted Diarization  
This section reports the performance of our human-assisted cross-show speaker linking for the two baseline systems (_SincDS_ and _ResDS_) and the combination of both _Averaging_ and _No averaging_ speaker representations considering the ranking of _All_ speakers or only the _Nearest speaker per show_. All results are given in Figure 5. A first look at the Figure confirms that the _ResDS_ system strongly outperforms the _SincDS_ system due to the quality of the produced embeddings. We then observe that all four systems benefit from asking more questions to the human operator (improving final DER), and this for all speaker representations and when considering the entire list of speakers or only the nearest per show. According to the penalized DER (lightest bars), the benefit obtained when using the proposed active correc-tion method is higher than the cost of active correction (except for some cases in the _ResDS_ system using VAD). Compared to the baseline systems, asking seven questions per current speaker allows to reduce the incremental cross-show DER by a relative 24% for the _ResDS_ system using a VAD segmentation and all \(x\)-vectors from the _Averaging_ representation of past speakers and can be reduced by up to 35% for the case of _ResDS_ system using the reference segmentation with the _Averaging_ representation of the Nearest speaker per file. Now comparing the two speaker representations--_Averaging_ of _No-averaging_--we find that the _No-averaging_ representation always perform (at least slightly) better the _Averaging_ representation. This might be explained by the fact that averaging all \(x\)-vectors from a speaker per show merges robust \(x\)-vectors extracted from long clean segments with other \(x\)-vectors extracted from noisy and short segments that degrade the speaker representations. This effect is more clear for the _SincDS_ system which embedding's quality is lower than for the _ResDS_ system. Reducing the set of \(x\)-vectors to the nearest speaker per show does not provide any clear benefit. This experiment also shows that when using _ResDS_ system, active correction of the clustering is highly dependent on the quality of the segmentation, which is less the case for SincDS. For _ResDS_, by comparing the performance of active correction when using reference segmentation or an automatic one (the difference of dash line and bars in two below line of Figure 5), it can be observed that the proposed active correction process is more efficient with the reference segmentation. Indeed, when using an automatic segmentation, allowing more questions per speaker leads to an increase of \(DER_{pen}\) that ends to be higher than the baseline one. The benefit of active human correction does not compensate for its cost. Remember that the number of questions from 1 to 7 is an upper limit but in many practical cases this number is not reached. Future work will focus on active correction for segmentation. One could be surprised by the fact that the DER obtained by the _ResDS_ system on the Development set is lower when using an automatic segmentation than when using the reference. A first analysis shows that an automatic segmentation using this system generates a higher number of speakers in the within show hypothesis. Each of these speakers having thus a shorter speech duration, the clustering errors that occur during the cross-show diarization affect less the final DER when a speaker is misclassified. The same effect not being observed with the _SincDS_ system underline the fact that side effects of within show diarization errors on the cross-show diarization are complex and will be studied in further studies. After analysing the results obtained on the _Development_ set, we set the optimal configuration for each system using both reference or automatic segmentation, we also set a maximum number of questions per speaker in the current file for each system and perform the corresponding experiment.