## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IPA** & **ASCII** & \multicolumn{2}{c}{**Examples**} & \\  & & **Spanish** & **Basque** \\ \hline i & i & pico & ipar \\ u & u & duro & umore \\ e & e & pero & hemen \\ o & o & toro & hori \\ a & a & valle & kale \\ m & m & madre & ama \\ n & n & nuuca & neska \\ n & N & año & arraina \\ P & P & padre & apeza \\ b & b & bsa & begia \\ t & t & tomo & etorri \\ d & d & dedo & denda \\  & & casa & \\ k & k & queso & ekarri \\  & & kilo & \\ g & g & gata & gia \\ f & f & fatal & afaria \\  & & cero & - \\  & & & \\ s & s & sala & hasi \\ s’ & s & - & zoroa \\ f & s & - & kaivo \\ x & j & mujer & ijito \\ r & R & rosa & arrunta \\ r & r & puro & dirua \\ l & l & lejos & lana \\ f & X & mucho & tokia \\ ts’ & X & - & atzo \\ ts & X & - & mahatsa \\ c & X & - & t takun \\ \(\&\) & y & caballo & pilaka \\ j & y & hielo & - \\ j & y & convuge & - \\ j & y & - & joan \\ j & y & - & onddo \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reduced set of phonetic units for Spanish and Basque with examples. IPA units are shown as well as the simplified ASCII encoding used in this work. an implicit adaptation to speakers, acoustic conditions, vocabulary, etc. ), which is the main objective of this work. In [24], the authors also targeted BP plenary sessions, but adopted a different approach to leverage their speech contents, by creating two separate datasets for Spanish and Basque, on which two monolingual ASR systems were trained. As a result of this work, we obtained a speech database specifically targeted at BP sessions. The database includes a large amount (998 h) of speech data for training acoustic models, and a development dataset (comprising more than 17 h of speech) used for tuning and evaluation under a cross-validation scheme. This latter dataset was extracted from a separate set of more recent BP sessions (not included in training) and then manually audited (their transcriptions being edited to match the audio contents). Finally, a bilingual (aggregated) trigram language model, estimated from the original minutes and translations of BP plenary sessions in Spanish and Basque, is also provided. This paper is an extension of a previous work [25]. The primary purpose of that work was to collect speech data for Basque and Spanish (with particular emphasis on the former) using the Basque Parliament plenary sessions as source. Second, we also aimed to build a fully bilingual ASR system especially targeted at BP sessions, so that its output could be reliably used as a starting point to produce the minutes (which still required human supervision). In this paper, we provide new results and more in-depth analyses. The new contributions of this work with regard to [25] are summarized as follows: (1) the semi-supervised method employed to extract, rank and select training segments from BP sessions is now applied iteratively until the observed improvement is small enough; (2) to increase the statistical significance of performance results, the small (4 h long) development and test datasets used in [25] have been replaced by a larger (17 h long) development set which is used under a cross-validation scheme, with 20 random 50/50 partitions, to perform hyperparameter tuning and then compute ASR performance; (3) the hyperparameter tuning procedure is described in detail and (4) the results section is enriched with figures illustrating the convergence of the training process and the ranking of segment scores. The rest of the paper is organized as follows. Sections 2 and 3 describe the main components of our bilingual ASR system and the method used to extract, rank and select training segments, respectively. Section 4 provides the details of the experimental framework used to evaluate our fully bilingual ASR system on Basque Parliament data, while Section 5 presents and discusses the results obtained in cross-validation experiments on the new development dataset specifically created in this work. Finally, a summary of the paper, conclusions and further work are outlined in Section 6.