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Strong translation quality doesn't guarantee high speech or temporal fidelity, revealing critical gaps in existing evaluation practices for speech translation systems.
Unleashing LLMs' reasoning powers on speech unlocks a new ASR paradigm, slashing error rates by up to 17% simply by having the model "think" before transcribing.
