arXiv:2606.31947v1 Announce Type: new Abstract: State-of-the-art speech datasets predominantly focus on widely spoken languages, often overlooking low-resource languages such as Luxembourgish, which remain underrepresented in speech technology research. In this work, we introduce LuxEmo, a 21-hour conversational expressive speech corpus for Luxembourgish with 4 emotion categories. LuxEmo is derived from Radio T\'el\'evision Luxembourg (RTL) youth broadcasts, using automated detection followed by human validation. We propose a semi-automatic curation workflow combining voice activity detection,
The increasing focus on AI for low-resource languages reflects a broader trend toward inclusivity in technology and a recognition of the strategic importance of linguistic sovereignty in the digital age.
This development contributes to linguistic diversity in AI, potentially enabling advanced AI applications for smaller language communities and fostering local digital economies.
The availability of expressive speech datasets for low-resource languages like Luxembourgish facilitates the development of localized AI models, reducing dependency on larger language models and potentially increasing linguistic resilience.
- · Luxembourgish language speakers
- · AI developers in low-resource language communities
- · Governments focused on linguistic preservation
- · RTL (Radio Télévision Luxembourg)
- · Monolingual AI service providers
- · Companies neglecting linguistic diversity
Increased development of Luxembourgish-specific AI applications, such as voice assistants and automated services.
Enabled cultural preservation and digital empowerment for the Luxembourgish community, fostering a more robust local digital ecosystem.
Set a precedent for other low-resource language communities to develop similar datasets, pushing for a more decentralized and linguistically diverse global AI landscape.
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