Recently text and speech representation learning has successfully improved many language related tasks. However, all existing methods only learn from one input modality, while a unified acoustic and text representation is desired by many speech-related tasks such as speech translation. We propose a Fused Acoustic and Text Masked Language Model (FAT-MLM) which jointly learns a unified representation for both acoustic and text in-put. Within this cross modal representation learning framework, we further present an end-to-end model for Fused Acoustic and Text Speech Translation (FAT-ST). Experiments on three translation directions show that our proposed speech translation models fine-tuned from FAT-MLM substantially improve translation quality (+5.90 BLEU).