# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 6 Conclusions  
Through this work, our goal was the improvement of the speaker attribution problem when dealing with broadcast data. These data are characterized for their great variability--also defined as a collection of particular domains with individual characteristics. For speaker attribution improvement, we studied three subtasks: the impact of diarization in the results, the importance of domain mismatch in the approaches and the proposal of alternative approximations that are robust enough to manage unseen scenarios. With respect to the importance of diarization, our experiments with two straightforward approaches (direct and indirect assignment systems) confirm the benefits of using diarization labels (approximately a relative 34% improvement) despite being noisy (17% DER on the same dataset) when domain issues are canceled. Thus, the accumulation of acoustic information thanks to diarization significantly compensates the poor individual robustness of each obtained embedding due to its short length (around 3 s). However, domain issues are a real problem in broadcast data. The same experiments with adaptive oracle calibrations show improvements up to a relative 24% by considering more specific domains. By contrast, real calibration adjusted during development can significantly degrade the performance due to domain mismatch, in our case, up to a relative 73%. To solve it, we have proposed two alternatives: A new hybrid system that simultaneously performs diarization and speaker attribution as well as a semisupervised approach making use of a limited amount of annotated in-domain data. Regarding our hybrid alternative, it requires no calibration, showing great robustness against domain mismatch and obtaining relative improvements of 47% compared to the traditional counterparts. Moreover, this new approach has managed to overcome the results obtained with some adaptive oracle calibrations. With respect to the semisupervised approach, it manages to improve the performance in both unsupervised proposals, indirect and the new hybrid system. These improvements are particularly interesting in the indirect assignment system, which only needed a small portion of in-domain audio to boost its performance (relative 48% improvement). With respect to the hybrid system, its semisupervised version offers a much more reduced improvement (a relative 6%) but obtains the best results with the dataset. Conceptualization, I.V. and A.O. ; methodology, I.V. ; software, I.V. ; validation, I.V. ; formal analysis, I.V. and A.O. ; investigation, I.V. and A.O. ; resources, A.O., A.M. and E.L.; data curation, A.O. and E.L.; writing--original draft preparation, I.V. ; writing--review and editing, I.V. and A.O. ; visualization, I.V., A.O., A.M. and E.L.; supervision, A.O., A.M. and E.L.; project \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Data Subset**} & \multicolumn{2}{c}{**Unsupervised**} & \multicolumn{2}{c}{**Semisupervised**} \\  & **Indirect** & **Hybrid** & **Indirect** & **Hybrid** \\ \hline Dev. subset & **38.86** & 39.07 & 42.40 & **38.45** \\ Eval. subset & 59.00 & **30.56** & 30.66 & **28.74** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AER (%) results for the Albayzin 2020 corpus for the assisted configuration. Results from indirect assignment and hybrid systems, including results for both development and test subsets. Experiment corresponds to an open-set condition. administration, A.O., A.M. and E.L.; funding acquisition, A.O., A.M. and E.L. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie Grant 101007666; in part by FEDER/Spanish Ministry of Economy and Competitiveness under Grant TIN2017-85854-C4-1-R, and in part by the Government of Aragon under Grant Group T36_20R.