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Neural network pruning is typically framed as a post-training compression technique. We show that for encoder-decoder ASR Transformers, one-shot magnitude pruning can act as a strong implicit regularizer: removing redundant parameters improves generalization without fine-tuning. Using Whisper-small, we introduce a sensitivity diagnostic combining gradient and Fisher criteria to identify pruning-fragile vs. pruning-resilient components. This reveals an encoder-decoder asymmetry: decoder FFNs are pruning-fragile, whereas decoder self-attention and late encoder layers contain removable redundancy. Without fine-tuning, pruning 50% of decoder self-attention improves WER by 2.38% absolute on LibriSpeech test-other; pruning the last four encoder layers at 50% yields 1.72% improvement. Gains persist on Common Voice and TED-LIUM, demonstrating cross-corpus generalization. At 40.8% sparsity, sensitivity-aware compression preserves near-baseline accuracy where global magnitude pruning collapses.