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End-to-end speech translation (E2E ST) aims to directly convert speech in a source language into text in a target language, and its performance is constrained by the inherent modality gap. Existing methods attempt to align speech and text representations to perform cross-modal mixup at the token level, which overlooks the impact of redundant speech information. In this paper, we propose cross-modal mixup with speech purification for speech translation (CMSP-ST) to address this issue. Specifically, we remove the non-content features from speech through orthogonal projection and extract the purified speech features for cross-modal mixup. Additionally, we employ adversarial training under the Soft Alignment (S-Align) to relax the alignment granularity and improve robustness. Experimental results on the MuST-C En-De, CoVoST-2 Fr-En, and CoVoST-2 De-En benchmarks demonstrate that CMSP-ST effectively improves the speech translation performance of existing cross-modal mixup methods.