A Multilingual Speech-Based Driver Assistant for B asque and E nglish (2025)

Abstract: This demo paper presents a prototype of a multilingual, speech-based driver assistant, designed to support both English and Basque languages. The inclusion of Basque—a low-resource language with limited domain-specific training data—marks a significant contribution, as publicly available AI models, including Large Language Models, often underperform for such languages compared to high-resource languages like English. Despite these challenges, our system demonstrates robust performance, successfully understanding user queries and delivering rapid responses in a demanding environment: a car simulator. Notably, the system achieves comparable performance in both English and Basque, showcasing its effectiveness in addressing linguistic disparities in AI-driven applications. A demo of our prototype will be available in the workshop.

## introduction speech-based driver assistants have become increasingly prevalent in modern vehicles, offering convenience and safety by enabling handsfree interaction with in-car systems. prominent examples such as amazon alexa auto or apple carplay, have demonstrated the potential of such technologies to enhance user experience by providing real-time information about navigation, vehicle status, and other essential tasks @xcite . these systems leverage advancements in natural language processing (nlp) and speech recognition, often relying on large language models (llms) trained on extensive datasets mainly containing data in high-resource languages like english @xcite . however, due to the scarcity of domain-specific datasets and models, the de-velopment of similar systems for low-resource languages remains a significant challenge. low-resource languages often lack the domain-specific annotated data necessary to train state-of-the-art nlp and speech processing models, leading to a greater reliance on rule-based approaches @xcite . basque is an isolated language spoken by approximately 750,000 people, with complex morphology and free word order. despite recent progress, publicly available basque language ai models such as the latxa series @xcite or llama-eus @xcite still underperform compared to models for highresource languages. additionally, there are limited datasets and tools tailored for specific applications, such as automotive environments, further complicating the development of basque-focused driver assistants. in fact, we have had to develop an automotive industry-related dataset from scratch for this project, since most publicly available and related datasets are in english or high-resource languages @xcite . this demo paper presents a multilingual speech-based driver assistant developed within the adapt-ia project, designed to provide support in both basque and english. integrated into an industrial car simulator, the assistant provides conversational access to real-time vehicle data, including speed, traffic conditions, tire pressure, and battery status. by creating a domain-specific corpus in basque and leveraging state-of-the-art nlp and speech processing techniques, our system, which will be shown in a demo in the workshop, demonstrates comparable performance in basque and english. this showcases the feasibility of extending driver assistant technology to underrepresented languages like basque. the rest of the paper is organised as fol-lows: section 2 provides an overview of the context and motivation behind this work. section 3 describes the corpus built to develop our assistant, and its architecture and implementation. section 4 presents a sample dialogue that showcases its capabilities. finally, section 5 presents our conclusions. ## context this work has been carried out within the adapt-ia project, which aims to develop ai technologies for the basque language, and others if the specific use case so requires. more particularly, the primary goal is to explore the development and integration of these technologies into the needs and specific use cases of various basque industrial sectors, such as machinetools manufacturing, the energy sector, the railway sector and the automotive industry. one sector where these advancements have significant potential is the automotive industry. as vehicles become increasingly reliant on intelligent systems, the need for speechbased technologies in different languages, including basque, becomes critical. in this context, this work represents a step towards providing drivers with the capabilities to naturally interact with smart vehicles in basque and english languages. to this end, we have developed a driving assistant that suits the automotive intelligence center (aic) industrial car simulator in the basque country: an hexapod system equipped with a fully operational cockpit interior from a road vehicle (see figure 1 ). with six degrees of freedom, this simulator is employed for dynamic analyses and serves as a platform for testing advanced driver-assistance systems and user communication strategies under highly realistic conditions. ## the adapt-ia speech-based driver assistant this section describes all the stages and tools created and integrated into the developed speech-based driver assistant, including the data generation process, in addition to the training of specific models for automatic speech recognition (asr), natural language understading (nlu), and text to speech (tts) that have been integrated into the final prototype. ## a sample conversation with the adapt-ia driver assistant ## conclusion we have presented a speech-based driving assistant capable of providing relevant real-time data to the driver in a conversational fashion, both in basque and english. to achieve this, we developed an organic domain-specific corpus in basque to train our system, effectively bridging the performance gap between the english and basque versions. this effort demonstrates the feasibility of extending similar nlp systems to low-resource languages, such as basque, thereby addressing the linguistic imbalance often found in ai technologies. our results demonstrate that tailored approaches can overcome resource limitations, enabling robust performance for underrepresented languages in specialized domains. this work not only advances the feasibility of developing basque-centric ai systems but also contributes to the broader goal of inclusivity in ai technologies for diverse linguistic communities.