This paper presents the methodology and outcomes of building collaborative speech corpora in Mozilla Common Voice (MCV), focusing on the Galician case within Proxecto Nós. We describe the organization of voice collection campaigns –on-site events, student participation, Validatón marathons, and corporate collaboration– and analyze the results in MCV v22.0. While the dataset has achieved a modest scale, major gaps remain in metadata completeness and dialectal tagging, with implications for ASR performance. Drawing on our experience, we highlight effective strategies for engagement, such as transparent communication, cultural identification, and user-friendly tools. We conclude with lessons learnt for improving data representativeness, participant retention, and ethical governance. The observations are specific to the Galician case study but may inform similar efforts in other lesser-resourced languages.

This paper introduces Nos_Brais-GL, a new open-access high-quality Galician speech corpus designed for the development of neural Text-to-Speech (TTS) systems. Nos_Brais-GL contains approximately 18 hours of professionally recorded male speech and a carefully curated set of utterances selected to ensure linguistic variation and phonetic and prosodic richness. Beyond its immediate application in synthetic speech generation, Nos_Brais-GL exemplifies good practices in TTS corpus design for lesser-resourced languages, emphasizing methodological transparency, open licensing, and interoperability.

The development of language technologies (LTs) such as machine translation, text analytics, and dialogue systems is essential in the current digital society, culture and economy. These LTs, widely supported in languages in high demand worldwide, such as English, are also necessary for smaller and less economically powerful languages, as they are a driving force in the democratization of the communities that use them due to their great social and cultural impact. As an example, dialogue systems allow us to communicate with machines in our own language; machine translation increases access to contents in different languages, thus facilitating intercultural relations; and text-to-speech and speech-to-text systems broaden different categories of users’ access to technology. In the case of Galician (co-official language, together with Spanish, in the autonomous region of Galicia, located in northwestern Spain), incorporating the language into state-of-the-art AI applications can not only significantly favor its prestige (a decisive factor in language normalization), but also guarantee citizens’ language rights, reduce social inequality, and narrow the digital divide. This is the main motivation behind the Nós Project (Proxecto Nós), which aims to have a significant contribution to the development of LTs in Galician (currently considered a low-resource language) by providing openly licensed resources, tools, and demonstrators in the area of intelligent technologies.