## 7 Human Assisted Cross-Show Diarization
### Cross-Show Question Generation Module  
Similarly to the human-assisted within-show speaker diarization, our method only focuses on speaker linking (clustering) and does not modify the segmentation nor the clustering obtained during the stage of within-show diarization. During the incremental cross-show speaker linking process described in Section 6, the automatic system selects a couple of speakers \((S_{i},S_{a}^{N+1})\) where \(S_{i}\) appeared in the past and \(S_{a}^{N+1}\) appeared in the current show. The human operator is then asked to listen to one speech sample from each speaker (\(S_{i}\) and \(S_{a}\)) and to answer the question: \"Are the two speech samples spoken by the same speaker? \"  
The human-assisted cross-show diarization correction process differs from the within-show as no clustering tree can be used to define the question. In the cross-show scenario, we decompose the task into two steps:  
1. **detection of recurrent speakers**, i.e., detect if a given speaker from the current file has been observed in the past;
2. **human-assisted closed-set identification** of speakers detected as _seen_ during the first step. Speakers who have not been categorized as _seen_ are simply added to the _known_ speaker database. To detect the recurrent speakers we propose to use a pseudo-distance matrix based on the one described in Section 6.1. The information conveyed in this matrix is the pseudo-distance between couples of \(x\)-vectors, the lower the pseudo-distance, the more likely two speakers are the same. This matrix is depicted in Figure 4.  
This matrix can be computed in different manners depending on the representation chosen for _known_-speakers. Based on the idea that a _known_-speaker owns many \(x\)-vectors in each show where they appear, we consider here three speaker representations:  
* The speaker can be represented by the average of her existing \(x\)-vectors (corresponding to all segments of this speaker in all already processed shows). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Segmentation**} & \multirow{2}{*}{**Data**} & \multicolumn{2}{c}{**DER**} \\ \cline{3-5}  & & & **Within** & **Cross-Show** \\ \hline \multirow{4}{*}{_SincDS_} & \multirow{2}{*}{Ref} & Dev & 17.77 & 53.38 \\ \cline{3-5}  & & Eval & 13.38 & 51.33 \\ \cline{2-5}  & & Dev & 19.07 & 53.75 \\ \cline{3-5}  & & Eval & 20.20 & 53.85 \\ \hline \multirow{4}{*}{_ResDS_} & \multirow{2}{*}{Ref} & Dev & 14.12 & 32.02 \\ \cline{3-5}  & & Eval & 10.63 & 30.43 \\ \cline{1-1} \cline{2-5}  & & Dev & 14.97 & 27.31 \\ \cline{1-1} \cline{2-5}  & & Eval & 16.74 & 31.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of the baseline cross-show diarization system applied on top of both within-show diarization baseline systems for both Development (Dev) and Evaluation sets (Eval). The performance is provided when using the reference segmentation (Ref) or an automatic segmentation (VAD) as a weighed average of within-show Diarization Error Rate (DER) and weighed average of incremental cross-show DER (i.e., computed for each show and weighed according to the duration of the shows). * A second representation, referred to as _Averaging_ in the remaining of this study, consists of a set of one \(x\)-vector per file in which this speaker appears. The \(x\)-vector for a show being the averaged of all \(x\)-vector for this speaker in the given show. * Eventually, we could keep, to represent a speaker, the set of all \(x\)-vectors belonging to this speaker (one \(x\)-vector per segment). We will later on refer to this last approach as _No averaging_.