## 6 Summary and Future Work  
In this paper, a new bilingual Basque-Spanish speech database has been presented. The database was extracted from Basque Parliament plenary sessions with the aim of helping the development of speech technology for the Basque language, which is relatively low-resourced. However, the database is well-suited for the development of bilingual ASR systems capable of decoding speech signals in both Basque and Spanish and that would seamlessly transition between languages. Given the similarity between Basque and Spanish at the phonetic/phonological level, the acoustic models can be shared by both languages, which circumvent the lack of training data for Basque. The ASR system becomes fully bilingual by using a single dictionary including words in Basque and Spanish and a single language model trained on texts in both languages. The database is designed specifically for processing Basque Parliament speeches, which implies that its performance may suffer when dealing with speech from other sources featuring diverse speakers, more variable channels and environments and different or more general domains. Thus, due to the limited number of speakers and the relatively stable conditions of plenary sessions in the Basque Parliament, our database should be supplemented with additional training datasets featuring a broader range of speakers, channels, environments, speech styles (such as conversational speech) and domains to develop ASR systems capable of transcribing speech in Spanish, Basque, or both languages under various conditions. The database consists of two subsets: the training and test datasets. For each dataset, an index file is provided with each line containing the information corresponding to a single segment: the audio filename, language and speaker tags, text-audio similarity score, segment length and transcription. The training set is aimed at estimating the acoustic models of an ASR system and was extracted from the 2013 to 2021 sessions, amounting to 1445 h of speech (1315 h if only highly reliable segments are considered) with a distribution strongly biased towards Spanish (72% Spanish, 28% Basque). The test set was extracted from five sessions held in February 2022 amounting to 17.2 h of speech and was split into a development set (7.6 h) for tuning purposes and an evaluation set (9.6 h) for measuring the performance of ASR systems. The distribution of languages in the test set is also biasedtowards Spanish (68% Spanish, 32% Basque). Both datasets are balanced with regard to gender. The paper also describes the procedure employed to build the training dataset, which involves: (1) tokenization of the audio signals and the transcriptions at the phonetic level by means of a phonetic decoder and an in-house grapheme-to-phoneme converter, respectively; (2) alignment of the phonetic sequences obtained from the audio signals and transcriptions; (3) an iterative segment extraction procedure which uses the alignment similarity as the main selection criterion. The test dataset was built by applying the same procedure followed by manual supervision of language tags and transcriptions. Finally, the Basque Parliament database has been characterized and validated by using it to build a state-of-the-art ASR system based on pre-trained Wav2 Vec 2.0 models finetuned on the training set and evaluated on the test set. The best overall performance was 2.90 WER and 1.20 CER, obtained when using an in-domain language model. It must be noted that these low error rates are due to an almost perfect matching between the test and training sets in terms of speakers, speech styles, environment and channel conditions and domain. Performance would degrade if our ASR system was tested on other unrelated datasets featuring different speakers, speech styles, channels, environments and/or domains. As noted above, additional training datasets covering the test conditions should be used in that case to supplement our database.