###### Abstract  
The development of speech technology requires large amounts of data to estimate the underlying models. Even when relying on large multilingual pre-trained models, some amount of task-specific data on the target language is needed to fine-tune those models and obtain competitive performance. In this paper, we present a bilingual Basque-Spanish dataset extracted from parliamentary sessions. The dataset is designed to develop and evaluate automatic speech recognition (ASR) systems but can be easily repurposed for other speech-processing tasks (such as speaker or language recognition). The paper first compares the two target languages, emphasizing their similarities at the acoustic-phonetic level, which sets the basis for sharing data and compensating for the relatively small amount of spoken resources available for Basque. Then, Basque Parliament plenary sessions are characterized in terms of organization, topics, speaker turns and the use of the two languages. The paper continues with the description of the data collection procedure (involving both speech and text), the audio formats and conversions along with the creation and postprocessing of text transcriptions and session minutes. Then, it describes the semi-supervised iterative procedure used to cut, rank and select the training segments and the manual supervision process employed to produce the test set. Finally, ASR experiments are presented using state-of-the-art technology to validate the dataset and to set a reference for future works. The datasets, along with models and recipes to reproduce the experiments reported in the paper, are released through _Hugging Face_. multilingual speech; basque; spanish; spoken language resources; low-resource languages; semisupervised learning; automatic speech recognition +
Footnote †: journal: Medical  
The first and most relevant goal of the work reported in this paper is to help fix the lack of speech data for Basque by releasing and making publicly available a speech dataset extracted from Basque Parliament plenary session recordings. The main part of the database, corresponding to the training dataset, consists of a 1445 h long set of transcribed segments of three types: monolingual in Basque, monolingual in Spanish and bilingual in Basque and Spanish. A separate manually supervised 17-h-long set of segments is also provided. This set can be utilized for fine-tuning hyperparameters and/or for evaluating automatic speech recognition (ASR) performance in both languages. Finally, two bilingual n-gram language models are provided too. The first one is a trigram model obtained from Wikipedia pages in Spanish and Basque and aims to support the development of general-purpose ASR systems. The second one is a 4-gram model obtained from Basque Parliament transcriptions in the period 2013-2021 and aims to support the development of ASR systems for the Basque Parliament domain. A second goal of this work is to leverage Spanish data to improve speech technology for Basque. This is the reason why everything is designed to be bilingual, i.e., the datasets, the acoustic models and the language models. In contrast to more common approaches to low-resource ASR in which multilingual datasets or models are adapted (finetuned) to a new low-resource language based on a relatively small set of transcribed speech segments [19; 20; 21], our approach consists of building a fully bilingual ASR system from scratch. The similarity of Basque and Spanish at the acoustic-phonetic level allows us to represent the sounds of both languages with a single (simplified and small) set of units. On the other hand, their large differences at the lexical and syntactic levels allow us to use a single language model (trained on Basque and Spanish texts) that naturally switches from one language to another based only on the acoustic-phonetic clues found by the acoustic models.