arXiv:2512.16401v5 Announce Type: replace Abstract: Automatic Speech Recognition (ASR) can significantly reduce documentation burden in clinical workflows, but standard models degrade sharply in real-world telephony settings where noisy audio, dialectal variation, and strict data residency constraints prevent cloud-based adaptation. We study this "reality gap" using Gram Vaani: a telephonic Hindi corpus spanning rural healthcare and agricultural helplines, as the closest available proxy for clinical speech under strict on-device constraints. We show that a robust multilingual model (IndicWav2V
The increasing demand for practical AI solutions in sensitive sectors like healthcare, coupled with growing concerns over data residency and privacy, drives the development of on-device adaptation for ASR.
This development addresses critical barriers to AI adoption in regulated industries by enabling local processing, thereby reducing dependency on cloud infrastructure and enhancing data security.
The ability to perform on-device, continual adaptation significantly broadens the applicability and robustness of ASR technologies in environments previously deemed unsuitable due to data handling constraints.
- · Healthcare providers
- · AI developers specializing in edge computing
- · Patients in remote or sensitive areas
- · Regulatory bodies focused on data privacy
- · Cloud-centric ASR providers without on-device solutions
- · Legacy medical transcription services
Improved accuracy and adoption of ASR in clinical telephony, leading to reduced administrative burden.
Accelerated development of other on-device AI solutions for highly regulated sectors, fostering a decentralized AI growth model.
Potential for new business models focusing on localized, secure AI deployments, reducing global dependencies on large cloud providers.
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Read at arXiv cs.CL