## 3 Collecting Data from the Basque Parliament
### Processing Audio Resources  
To build the training dataset, we gathered videos from 408 plenary sessions, totaling 2123.86 h. Each session may consist of one or several videos depending on its duration. The sessions were collected from January 2013 to December 2021, spanning three parliamentary periods, as follows: 173 sessions (9-181) from the 10 th Legislature (November 2012-October 2016), 160 sessions (1-160) from the 11 th Legislature (October 2016-August 2020), and 75 sessions (1-75) from the 12 th Legislature (August 2020-...). Each legislature includes different representatives and government counselors, meaning that a different (but partially overlapping) set of speakers participated in each period. Over the entire 2013-2021 period, the dataset comprises 190 different speakers. To build the test dataset, we collected videos from five plenary sessions held in February 2022 (sessions 77 to 81 of the 12 th Legislature), amounting to 26.38 h and involving 57 speakers, 56 of whom had previously appeared in the training set. In total, the database includes 191 speakers with a nearly even distribution of 99 men and 92 women, providing a balanced representation of speaker gender (see Figure 1). Details about the raw materials used to create the training and test datasets are summarized in Table 2.  
Figure 1: Distribution of male and female speakers by year. Years 2013–2021 include the sessions used to build the training set. Year 2022 includes the five sessions used to build the test set. Note that since almost all speakers in the test set also appear in the training set, the test set is not independent from the training set and ASR performance computed on the test set will be biased, because the models estimated on the training set will be intrinsically adapted to those speakers. However, this speaker dependence is consistent with our primary goal of developing and optimizing an ASR system for the Basque Parliament domain. Since acoustic models will be periodically updated with the latest sessions (including speeches from speakers that will likely appear in the upcoming sessions), our ASR performance scores will closely reflect the performance that can be expected in practice. Audios were all recorded through stand microphones which could be suitably directed towards the speaker so that the signal-to-noise ratio was always high. As far as we know, the microphones and the recording hardware did not change during the period we made the recordings. However, the audio/video format did change: until the third session of the 11 th Legislature (10 November 2016), we received RealMedia videos, the audio channel using a Cook Audio codec with a single channel at 22,050 Hz and 32 bits per sample; starting from the fourth session of the 11 th Legislature (23 November 2016), we received MP4 videos, the audio channel using an MPEG AAC audio codec with 2 channels (stereo) at 48,000 Hz and 32 bits per sample. However, since all audios were later converted to WAV format, PCM encoding, single channel at 16,000 Hz and 16 bits per sample (by means of ffmpeg, see [[https://ffmpeg.org/](https://ffmpeg.org/)]([https://ffmpeg.org/](https://ffmpeg.org/)), accessed on 24 February 2024), we hope that the switch from RealMedia to MP4 has not introduced any serious bias to our dataset. Another relevant fact to be noted is that during voting (that may be scheduled in the middle of a plenary session) the debate is interrupted, votes collected and counted and finally, the results are announced by the president. This process may take several minutes, during which no speech is recorded but instead a long quasi-silent interval is included in audio recordings. These long intervals of silence pose a challenge to the alignment procedure that is applied to synchronize the subtitles and to extract the training segments (see Sections 4.4 and 4.5 for details). Thus, specific countermeasures have been integrated into the alignment procedure to avoid including these silent intervals inside a segment.