## 1 Introduction  
In bilingual communities, speakers sometimes mix languages and jump spontaneously from one language to another, sometimes just for one word or phrase, sometimes for longer, and go back and forth several times [1]. This phenomenon, known as code switching, appears even in formal settings such as parliamentary sessions and raises some interesting problems from the point of view of automatic speech recognition (ASR) systems [2; 3; 4]. Commonly, each language requires a specific ASR system with its own phonetic, phonological, lexical and syntactic constraints. This means that language detection and segmentation (that is, language diarization) must be performed on code-switched speech before applying an ASR system [5; 6; 7]. This language identification and segmentation process adds complexity and computational cost, and may introduce unrecoverable ASR errors when language detection fails. Current efforts are being devoted to integrate code switching detection and ASR within end-to-end deep learning approaches [8; 9; 10]. In the last years, the interest in code switching has increased for certain language pairs, especially Mandarin-English, with international evaluations being organized [11] and open datasets being released [12]. In this work, we deal with Basque and Spanish (the two official languages in the Basque Country). Basque is a language of unknown origins, spoken by around 900 thousand speakers in a small region of Spain and France [13; 14]. Basque greatly differs from Spanish, especially at the lexical and syntactic levels. Only a relatively small number of words come from the Latin or Romance languages with which Basque has had contact (Spanish, French and, of course, Latin itself). Spanish (like English) builds its structures using individual words with different grammatical functions, while Basque uses a set of cases to mark the grammatical relationships between words in a sentence, each with specific syntactic functions, and words are built in an agglutinative way by adding suffixes to lexemes. Spanish uses a verb conjugation system based on person and number, while Basque includes markers for subject, object and indirect object within the verb itself. In declarative sentences, the most common order in Spanish is subject-verb-object while in Basque it is subject-object-verb. However, on a phonetic level, Basque shares many of its sounds with Spanish (including its five vowels), with only some consonants, such as /ts/, /ts/, /s/ and some other less frequent ones (see Table 1) not appearing in Spanish [15]. In fact, Basque and the variety of Spanish spoken in the Basque Country share a great deal of features at the acoustic level, which allows us to use a single set of models, able to process speech in both languages so that a code switched transcription would be naturally output. Our proposed ASR system includes a single set of acoustic models, a single vocabulary (including words in both languages, sometimes with the same transcriptions but different pronunciations, sometimes with different transcriptions but the same pronunciations) and a single (aggregated) language model, which accepts code switchings at any point. A positive effect of this integrated approach is that sharing acoustic models can alleviate the lack of annotated spoken resources for the low-resource language (Basque, in this case), by taking advantage of the resources available for the other. This will hopefully increase the robustness of the ASR system for the low-resource language, especially if the sets of acoustic units of the two languages are relatively close (as in the case of Basque and Spanish). On the negative side, having a single vocabulary may lead to a higher number of errors, due to words being recognized in the wrong language (those pronounced in the same way or very closely in the two languages).