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Machine translation benchmarks have functionally saturated, but pairing human-authored failure cases with deterministic verification rules reveals critical multimodal blind spots that automated metrics consistently miss.
SN-WER effectively mitigates script-induced error inflation in ASR evaluations, revealing true model performance gaps.
