# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 5 Results
### Semisupervised Solutions  
In the previous experiments, we have confirmed the alternative definition of broadcast as a collection of audios with particular characteristics. These individual properties generate an important domain mismatch that can cause significant harmful effects on the speaker attribution task.  
While, in the previous experiments, we developed an unsupervised technique, our hybrid system, robust enough to deal with unseen scenarios, we also want to evaluate other types of solutions. One of them are semisupervised alternatives, also known as human-assisted solutions, which require available small portions of annotated in-domain data.  
Bearing in mind those results from Table 2, we consider all the audios from the same show as the domain. This choice is done according to the trade-off between specificity and simplicity to gather the data. Then, for each show under analysis, we consider the audio and speaker labels of an episode available as adaptation in-domain data.  
Due to the way Albayzin 2020 data subsets are arranged, most shows are exclusive from a subset. Hence, we must alter the Albayzin 2020 subsets in order to reassure this annotated adaptation audio per show. The considered modification divides each subset, development and test, into two parts: The first part consists of the annotated audios, one per show in the subset, for adaptation purposes. In our experiments, we consider the first episode in chronological order for adaptation. The second part of the subset, with the remaining episodes, is considered as the new evaluation subset.  
The next experiments evaluate the indirect assignment system as well as the hybrid one in both unsupervised and human-assisted manners with the new subsets. The results of these experiments are contained in Table 4.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Subset**} & \multicolumn{3}{c}{**Closed Condition**} & \multicolumn{3}{c}{**Open Condition**} \\  & **Direct** & **Indirect** & **Hybrid** & **Direct** & **Indirect** & **Hybrid** \\ \hline Dev. subset & **13.73** & 15.27 & 15.89 & 41.91 & **37.45** & 37.68 \\ Eval. subset & 25.11 & 17.20 & **16.49** & 65.31 & 60.34 & **31.95** \\ \hline \hline \end{tabular}
\end{table}
Table 3: AER (%) results for the Albayzin 2020 corpus. Results of direct and indirect assignment as well as the hybrid systems, including results for both development and test subsets. Experiment corresponding to closed and open conditions.  
The results in Table 4 indicate the benefits of small available portions of annotated in-domain data. By only having a single hour or audio manually annotated, systems previously seen as weak against domain mismatch (such as the indirect assignment system) now obtain significant improvements (a relative 48%). Actually, the same indirect system, now with an adapted calibration, manages to obtain results similar to those obtained with oracle calibration for shows. With respect to the hybrid system, the obtained benefits are not as noticeable (a relative 6% improvement) but are the best results obtained in the whole study.