Low-Resource AI

TL;DR for operators A public model can come with weights, a dataset, configuration files, inference code, and evaluation tooling while still leaving another team unable to reconstruct what was trained, what was held out, how the released checkpoint was selected, or how broadly its performance claims apply. Hiwa Asadpour’s audit of a Central Kurdish text-to-speech release1 makes that problem concrete. The strongest evidence is not that the underlying research is invalid; the paper being audited is described as comparatively cautious and well documented. The problem appears in the handoff from publication to public artifacts. Configuration settings conflict with the reported run, evaluation assets are missing, the released dataset does not mark the original test utterances, and the model card states a stronger performance conclusion than the source paper supports. ...

Talk Is Cheap, Until It Trains ASR Call centers are very good at producing audio. They are much worse at producing clean, labeled, domain-matched, multi-speaker training data. That distinction matters. A business may have thousands of hours of customer calls, branch conversations, medical consultations, field-service recordings, or internal support audio. But most of it is noisy, consent-constrained, poorly transcribed, unevenly distributed across accents and topics, and inconveniently full of humans doing human things: interrupting, pausing, talking over each other, drifting off-topic, and using domain-specific shorthand as if the ASR model had attended the onboarding session. ...