## 4 Data Extraction
### Training Data Extraction
The average WER figures obtained on the tuning and evaluation subsets by the three ASR systems in cross-validation experiments are shown in Table 4, disaggregated per language. Significant performance improvements are observed when comparing baseline models to first-iteration models: the average WER goes from 16.44% to 4.29% on the tuning sets, meaning a 73.9% relative reduction in WER, and from 16.57% to 4.41% on the test sets, meaning a 73.4% relative reduction in WER. These improvements are largely due to the increased volume of training data (998 h compared to 332) and especially to the domain-specific nature of the training material, as Basque Parliament data are employed for both training and evaluating the ASR systems. However, second-iteration models perform only slightly better than first-iteration models, suggesting that further iterations of the data extraction pipeline would likely yield even smaller improvements. Therefore, no more iterations are performed. Due to format issues, the Basque Parliament sessions from 2013 were not included in the experiments reported above. Later, the format issues were fixed and the 2013 sessions were integrated into our datasets. To accomplish that, the models used to obtain the second-iteration set of segments were used to perform a single iteration of the data extraction procedure on 2013 sessions and the resulting set of segments, \(S_{2013}^{(2)}\), were added to \(S_{train}^{(2)}\) to obtain the final set of training segments: \(S_{train}=S_{train}^{(2)}\cup S_{2013}^{(2)}\). Note, that \(S_{train}\) includes _all_ of the segments retrieved from the 2013 to 2021 Basque Parliament sessions without applying any similarity threshold, which means that for some segments the transcription will not completely correspond to the audio contents. This could negatively affect the quality of the resulting models. To discard unreliable segments, a more restrictive training set, called _train-clean_ (\(S_{train-clean}\)) has been also defined by keeping only those segments for which \(AS\geq 95\). Note, that not all speakers participating in Basque Parliament sessions are represented in these sets because the segments of some of the speakers were discarded during the data extraction procedure. Out of 191 speakers participating in Basque Parliament sessions, 187 are represented in the training set; on the other hand, the dev and eval subsets of the test set contain 47 and 42 different speakers, respectively, all of them being also represented in the train and train-clean sets. Table 5 shows the duration (in hours) and the number of \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Set** & **Iteration** & **Basque** & **Spanish** & **Bilingual** & **All** \\ \hline \multirow{3}{*}{Tuning} & 0 & 16.63 & 16.19 & 22.38 & 16.44 \\  & 1 & 5.43 & 3.93 & 4.38 & 4.29 \\  & 2 & 5.09 & 3.66 & 3.95 & 4.02 \\ \hline \multirow{3}{*}{Evaluation} & 0 & 16.57 & 16.38 & 22.44 & 16.57 \\  & 1 & 5.51 & 4.04 & 4.35 & 4.41 \\  & 2 & 5.13 & 3.66 & 3.90 & 4.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average WER performance, disaggregated per language, in cross-validation experiments (using 20 random partitions) on the tuning and evaluation sets for the baseline (out-of-domain) models (iteration 0) and models trained on the segments obtained after the first and second iterations of the segment extraction procedure (in-domain models). segments, disaggregated per language, for all datasets of the Basque Parliament database: train, train-clean, development and evaluation.