arXiv:2606.11542v1 Announce Type: new Abstract: Modern pretrained self-supervised automatic speech recognition models are trained on large-scale audio data to encode speech into contextualized representations. However, their training data are heavily skewed toward high-resource languages with little data from low-resource languages, raising concerns about the potential underrepresentation of typologically uncommon speech sounds such as click consonants primarily found in Khoisan languages. This leads to our central research question: Can these models recognize click consonants as accurately as
The accelerating pace of AI development and model scaling, alongside growing scrutiny over data biases, brings this issue to the forefront.
It highlights a critical capability improvement in foundational AI models, reducing data dependency for underrepresented linguistic features and broadening AI applicability.
AI models are becoming more architecturally robust to typological diversity in speech, potentially enabling more equitable access and development for low-resource languages.
- · AI developers focused on multilingual and low-resource language applications
- · Populations speaking low-resource languages
- · Speech technology researchers
- · Open-source AI advocates
- · Monolingual AI development approaches
- · Companies reliant on large, perfectly aligned datasets for specific language nic
Self-supervised speech models will process a wider array of human phonetics more accurately, even for sounds not explicitly in their training data.
This capability reduces bias and enhances the applicability of AI speech technologies in diverse linguistic environments, especially in previously underserved regions.
It could accelerate the development of personalized education, healthcare, and communication tools for marginalized language communities, fostering greater digital inclusion and potentially geopolitical stability.
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Read at arXiv cs.CL