# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 2 IberSpeech-RTVE 2022 Evaluation Databases
### SPARL22 Database  
The SPARL22 database consists of spontaneous speech from Spanish parliament sessions held from 2016 up to now and amounts to about 2 h of speech extracted from 14 audio files. The timestamps for the roughly 1600 occurrences of the queries used as test data were also provided.  
The original recordings are videos in MPEG format. The evaluation organizers extracted the audio from these videos and converted them to PCM, 16 kHz, single channel and 16 bits per sample using the ffmpeg 4 tool ([[https://ffmpeg.org/](https://ffmpeg.org/)]([https://ffmpeg.org/](https://ffmpeg.org/)) (accessed on 24 July 2023)). It is worth mentioning that this database contains several noise types (e.g., laughing, applause, etc.), which makes it quite challenging.  
This database only provides test data to measure the generalization capability of the systems in an unseen domain in training and development.  
A summary of the main database features such as the number of word occurrences, the duration, and the number of speakers is presented in Table 8. The number of terms and the number of total occurrences both for STD and QbE STD tasks for the SPARL22 database are presented in Table 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**File ID** & **Data Type** & **\#occ.** & **dur. (min)** & **\#spk.** \\ \hline LN24H-20151125 & dev2 & 21,049 & 123.50 & 22 \\ \hline LN24H-20151201 & dev2 & 19,727 & 112.43 & 16 \\ \hline LN24H-20160112 & dev2 & 18,617 & 110.40 & 19 \\ \hline LN24H-20160121 & dev2 & 18,215 & 120.33 & 18 \\ \hline millennium-20170522 & dev2 & 8330 & 56.50 & 9 \\ \hline millennium-20170529 & dev2 & 8812 & 57.95 & 10 \\ \hline millennium-20170626 & dev2 & 7976 & 55.68 & 14 \\ \hline millennium-20171009 & dev2 & 9863 & 58.78 & 12 \\ \hline millennium-20171106 & dev2 & 8498 & 59.57 & 16 \\ \hline millennium-20171204 & dev2 & 9280 & 60.25 & 10 \\ \hline millennium-20171211 & dev2 & 9502 & 59.70 & 12 \\ \hline millennium-20171218 & dev2 & 9386 & 55.55 & 15 \\ \hline DG00090476 & test & 9683 & 60.65 & 52 \\ \hline DG90266390 & test & 8127 & 59.37 & 21 \\ \hline DG90715506 & test & 4533 & 30.06 & 46 \\ \hline DG90721106 & test & 4966 & 28.87 & 22 \\ \hline DG90734223 & test & 9518 & 59.03 & 55 \\ \hline I00920573 & test & 8397 & 51.46 & 77 \\ \hline ALL & dev2 & 149,255 & 930.64 & 173 \\ \hline ALL & test & 45,224 & 289.44 & 273 \\ \hline \end{tabular}
\end{table}
Table 6: RTVE2022-SoS database: number of word occurrences (#occ.), duration (dur.) in minutes (min.), and number of speakers (#spk.) development (dev2) and testing (test) datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Task** & **\#dev. Terms** & **\#dev. Occurrences** & **\#test Terms** & **\#test Occurrences** \\ \hline STD & 398 & 1502 & 260 & 1039 \\ \hline QbE STD & 103 & 574 & 107 & 896 \\ \hline \end{tabular}
\end{table}
Table 7: RTVE2022-SoS database query information: number of terms and number of occurrences for STD and QbE STD tasks both for development and test datasets (‘dev.’ stands for development, specifically for the dev2 dataset). The term length of the development query lists varies between 4 and 25 graphemes. The term length of the RTVE test query lists varies between 4 and 27 graphemes.