## 6 Automatic Cross-Show Diarization Baseline System
### Baseline System Description  
For incremental cross-show speaker linking, we assume that at time \(T\), a number \(N\) of shows, \(\{F_{1},F_{2},...F_{N}\}\), has already been processed by the automatic system to produce a \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Stopping Criteria**} & \multicolumn{3}{c}{**Dev**} & \multicolumn{3}{c}{**Eval**} \\ \cline{3-8}  & & \(\mathbf{DER}\) & **Avg. \#Q/h** & \(\mathbf{DER_{pen}}\) & \(\mathbf{DER}\) & **Avg. \#Q/h** & \(\mathbf{DER_{pen}}\) \\ \hline Baseline & - & 14.12 & - & - & 10.63 & - & - \\ \hline \multirow{2}{*}{Longest} & \(\mathbf{2c}\) & 12.78 & 21.65 & 16.39 & 9.42 & 24.02 & 13.42 \\ \cline{2-8}  & \(\mathbf{All}\) & 11.93 & 27.09 & 16.45 & 8.15 & 29.96 & 13.14 \\ \hline \multirow{2}{*}{Cluster center} & \(\mathbf{2c}\) & 14.56 & 21.29 & 18.11 & 9.42 & 24.17 & 13.45 \\ \cline{2-8}  & \(\mathbf{All}\) & 12.71 & 26.90 & 17.19 & 8.58 & 29.98 & 13.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: DER improvement using different stopping criteria and segment selection methods on the Development and Evaluation sets for the _ResDS_ model using the reference segmentation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Stopping Criteria**} & \multicolumn{3}{c}{**Dev**} & \multicolumn{3}{c}{**Eval**} \\ \cline{3-8}  & & \(\mathbf{DER}\) & **Avg. \#Q/h** & \(\mathbf{DER_{pen}}\) & \(\mathbf{DER}\) & **Avg. \#Q/h** & \(\mathbf{DER_{pen}}\) \\ \hline Baseline & - & 14.12 & - & - & 10.63 & - & - \\ \hline Baseline & - & 17.77 & - & - & 13.38 & - & - \\ \hline \multirow{2}{*}{Longest} & \(\mathbf{2c}\) & 15.29 & 7.00 & 16.45 & 11.06 & 7.07 & 12.24 \\ \cline{2-8}  & \(\mathbf{All}\) & 14.29 & 25.55 & 18.54 & 9.96 & 27.16 & 14.4 \\ \hline \multirow{2}{*}{Cluster center} & \(\mathbf{2c}\) & 15.66 & 7.03 & 16.83 & 10.86 & 7.12 & 12.05 \\ \cline{2-8}  & \(\mathbf{All}\) & 15.19 & 25.60 & 19.45 & 9.82 & 27.17 & 14.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: DER improvement using different stopping criteria and segment selection methods on the Development and Evaluation sets for the _SincDS_ model using the reference segmentation. cross-show diarization hypothesis including a set of \(M\) detected speakers, referred to as _known_-speakers. A database of \(x\)-vectors is built by including one single \(x\)-vector per _known_-speaker and per show, resulting in a collection of \(x\)-vectors: \(\{S_{1}^{1},S_{2}^{1},S_{3}^{1},S_{4}^{2},S_{5}^{2},S_{1}^{3},...S_{M}^{N}\}\), where \(S_{j}^{i}\) is the \(x\)-vector of speaker \(j\) observed in show \(i\). Note that a single speaker \(k\) might have appeared in several shows \(F_{n},F_{m}\) and thus be represented in the \(x\)-vector database by several \(x\)-vectors \(\{S_{k}^{n},S_{k}^{m}\}\). When receiving a new show, \(F_{N+1}\), the within-show speaker diarization is performed. All _new_-speakers, detected in this show, are then compared to the _known-speakers_ from the existing database in order to detect recurrent speakers and link them with their previous occurrences in the collection. For each speaker \(a\) detected in the current show, all segments attributed to \(a\) in this show are processed to extract a collection of \(x\)-vectors that are then averaged to obtain one single \(x\)-vector \(S_{a}^{N+1}\) representing this speaker. As a result, the current file produces a set \(\{S_{a}^{N+1},...,S_{A}^{N+1}\}\) of \(x\)-vectors. For comparison of _known_-speakers and _new_-speakers, all \(x\)-vectors extracted from multiple shows for a same _known_-speaker are averaged to produce one single \(x\)-vector for this speaker. A pseudo-distance matrix is computed by comparing the _new_-speakers to the _known_-speakers using a PLDA model. A verification score is computed for each pair of (_known_, _new_)-speaker. The verification score is then multiplied by \(-1\) and shifted so that the minimum of pseudo-distances is 0.