## 5 Results
### In-Domain Models (Retrained on Basque Parliament Data)  
The next step of the process consisted of using the speech segments of the BP plenary sessions with PRR \(\geq\) 80, which amount to around 998 h after discarding segments with a few number of phonetic units, to train a new set of acoustic models. Note that the baseline acoustic models were trained on around 332 h obtained from different and heterogeneous sources, which had nothing to do with BP sessions. At this point, however, we were using three times more training data; moreover, the data were extracted from BP sessions, under the same acoustic conditions and probably including some of the speakers that would appear on the datasets used to tune and evaluate the ASR system. Adapting to the operating conditions was, in fact, one of our main objectives, that is, taking advantage of the speech available from BP sessions to improve the performance of our ASR system when dealing with BP speech. Average WER figures (disaggregated per language) obtained by the retrained models in cross-validation experiments are presented in Table 6. Large and consistent improvements can be observed with regard to the results shown in Table 5. The global WER goes from 16.44% to 4.29% on the tuning sets (meaning a 73.9% relative WER reduction) and from 16.57% to 4.41% on the test sets (meaning a 73.4% relative WER reduction). Again, as expected, results are slightly better on the tuning set than on the test set. These improvements could be generally due to using a greater amount of training data and these data \begin{table}
\begin{tabular}{c c c} \hline \hline
**PRR Threshold** & **Time (Hours)** \\ \hline
100 & 186 \\
95 & 490 \\
90 & 745 \\
85 & 902 \\
80 & 1000 \\
75 & 1054 \\
70 & 1084 \\
65 & 1100 \\
60 & 1108 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Amount of speech (in hours) obtained from the training set by applying the baseline phone recognizer and keeping segments with PRR \(\geq\) Threshold. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Set** & **Metric** & **Basque** & **Spanish** & **Bilingual** & **All** \\ \hline \multirow{3}{*}{Tuning} & Avg & 16.63 & 16.19 & 22.38 & 16.44 \\  & StdDev & 0.79 & 0.40 & 0.48 & 0.38 \\  & 95\% CI & 0.35 & 0.17 & 0.21 & 0.17 \\ \hline \multirow{3}{*}{Test} & Avg & 16.57 & 16.38 & 22.44 & 16.57 \\  & StdDev & 0.86 & 0.45 & 0.51 & 0.46 \\ \cline{1-1}  & 95\% CI & 0.38 & 0.20 & 0.22 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: WER performance of baseline acoustic models in cross-validation experiments. being _in-domain_, that is, the same speakers and environment/channel conditions appear in both training and test datasets. On the other hand, WER figures are better for Spanish than for Basque. This could be explained by different factors: (1) the dominance of Spanish over Basque (with a 2:1 factor) in the training set; (2) a higher variability of accents/dialects in Basque (with not only different pronunciations, but also different vocabularies) compared to Spanish, which features a single accent/dialect in the dataset; and (3) the use of a reduced set of acoustic units might be hindering the discrimination ability of the acoustic models _only for Basque_, because the fused consonants do not exist in Spanish. Figure 2 shows a schematic of what we have done so far: using the baseline models and the models obtained after one iteration of the semisupervised data collection method on the BP training set to run cross-validation experiments, which involve hyperparameter tuning and ASR evaluation on independent BP datasets. Next, we proceed with a second iteration of the process, using the updated models to run the whole data collection pipeline and obtain a new set of hopefully better aligned segments. While running this second iteration, we may decide either to keep the same amount of data as in the first iteration (998 h, which corresponds to using a PRR threshold of 98.28) or instead to apply an alternative threshold (PRR \(>\) 95) to collect 1118 h (see Figure 3).