Research·arXiv cs.CL·1d ago

Framework maps how ASR systems encode linguistic hierarchies

Researchers propose a framework for auditing speech recognition systems through a decolonial lens, arguing that ASR failures in low-resource and Indigenous languages reflect embedded policy choices rather than technical inevitability. The work introduces a taxonomy of three failure modes (Misrecognition, Misalignment, Mistrust) and a seven-layer situatedness model to map how training data, evaluation metrics, and model assumptions encode linguistic hierarchies. This shifts the conversation from engineering parity to structural accountability, directly affecting how practitioners design voice interfaces for public services and healthcare where ASR mediates access.

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Explainer

The framework explicitly names policy as the root cause of ASR failure in low-resource languages, not data scarcity or model capacity. This shifts accountability from 'we don't have enough training data' to 'we made choices about which languages to optimize for.'

This connects directly to TreeProbe's methodology from August 1st, which operationalized cultural bias evaluation using native epistemic structures rather than external metrics. Both papers reject the assumption that scaling alone ensures equitable deployment. The decolonial ASR work also echoes the SAGA framework's focus on low-resource language communities, though SAGA sidesteps annotation costs while this paper interrogates the structural reasons those communities were underserved in the first place. Where SAGA asks 'how do we build better models for these languages,' the current paper asks 'why were these languages deprioritized in system design.'

If practitioners adopt the Three Harms taxonomy in public procurement requirements for voice-enabled healthcare or government services in the next 12 months, that signals the framework moved beyond academic critique into policy. If major ASR vendors (Google, Amazon, OpenAI) publish decolonial audits of their own systems using this framework by Q1 2027, that confirms structural accountability is becoming expected, not optional.

Coverage we drew on

TreeProbe : A Tibetan Medicine Benchmark for Cultural Bias in LLMs · arXiv cs.CL

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MentionsAutomatic Speech Recognition (ASR) · Indigenous languages · Three Harms taxonomy · decolonial computing

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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Decolonizing Linguistic Policies in Automated Speech Recognition: A Framework for Cross-Culturally Competent Speech AI”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.