## 8 Conclusions  
The benefit of human active correction for speaker diarization has been investigated for both within-show diarization and incremental cross-show diarization. This preliminary study has focused on an active correction of HAC clustering errors for the case of within-show diarization and active correction of speaker linking for incremental cross-show diarization. For within-show diarization, starting from a strong automatic baseline, we proposed two criteria to ask questions to a human expert. Five methods to select samples for auditory tests have been proposed and evaluated using a large and challenging dataset that will be publicly released. Performance of our human assisted speaker diarization system have been evaluated by using a penalized DER proposed in [36] and shows that it can decrease DER by up to 22.29% relative when applying active correction with the _2c criterion_. This leads to a reduction of 32.07% relative without taking into account the cost of human interaction. The second proposed stopping criterion (_All_) can achieve a relative reduction of 36.51% of DER but requires a higher and less efficient human effort. For incremental cross-show speaker diarization, we propose a baseline system and an active correction process based on a two step process: detection of previously seen speakers and human-assisted speaker identification. In the best case, our approach reduces the incremental cross-show DER by a relative 33.29% and the Penalized DER by a relative 11.79%. This preliminary study is very promising and opens large avenues for future studies. More analyses are ongoing to understand and refine the stopping criteria depending on the nature of the processed audio file and its difficulty for diarization systems. Current studies are conducted to improve the question generation module by estimating the quality of the question before soliciting the human expert. We are also developing the adaptation process in order to improve the automatic system using the information provided by the human expert. A limitation of this work comes from the restriction to HAC clustering when many works in the literature have been exploring active learning for other clustering algorithms. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**System** & \multicolumn{2}{c}{**SincNet**} & \multicolumn{2}{c}{**ResNet**} \\ \hline
**Segmentation** & **Ref** & **VAD** & **Ref** & **VAD** \\ \hline Speaker representation & \multicolumn{4}{c}{No-averaging} \\ \hline Ranked speakers & ALL & Nearest per file & \multicolumn{2}{c}{All} \\ \hline Limit of questions per new speaker & 2 & 2 & 4 & 2 \\ \hline Baseline DER & 51.33 & 53.85 & 30.43 & 31.13 \\ \hline DER & 33.78 & 37.74 & 20.30 & 23.88 \\ \hline DER penalized & 43.98 & 46.20 & 26.84 & 29.20 \\ \hline Relative improvement of DER & 34.19\% & 29.92\% & 33.29\% & 23.29\% \\ \hline Relative improvement of DER pen & 14.31 \% & 14.21\% & 11.79\% & 6.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of the human assisted cross-show diarization systems on the _Eval_ set for the best configuration of each baseline system using either reference or automatic segmentation. **Author Contributions:** Conceptualization, Y.P., M.S. and A.L. ; methodology, Y.P., M.S., A.L. and S.M. ; software, Y.P. and M.S. ; validation, Y.P., M.S., A.L. annd L.B. ; investigation, Y.P. and M.S. ; resources, A.L. and S.M. ; data curation, Y.P. ; writing--original draft preparation, Y.P., M.S. and A.L. writing--review and editing, A.L. and L.B; visualization, Y.P. and A.L. ; supervision, A.L. and L.B. ; project administration, A.L. ; funding acquisition, A.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This project has received funding from the CHIST-ERA project ALLIES (ARN-17-CHR2-0004-01) and the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 101007666, the Agency is not responsible for this results or use that may be made of the information.