Speech-image retrieval aims at learning the relevance between image and speech. Prior approaches are mainly based on bi-modal contrastive learning, which can not alleviate the cross-modal heterogeneous issue between visual and acoustic modalities well. To address this issue, we propose a visual-acoustic-semantic embedding (VASE) method. First, we propose a tri-modal ranking loss by taking advantage of semantic information corresponding to the acoustic data, which introduces the auxiliary alignment to enhance the alignment between image and speech. Second, we introduce a cycle-consistency loss based on feature reconstruction. It can further alleviate the heterogeneous issue between different data modalities (e.g., visual-acoustic, visual-textual and acoustic-textual). Extensive experiments have demonstrated the effectiveness of our proposed method. In addition, our VASE model achieves state-of-the-art performance on the speech-image retrieval task on the Flickr8K and Places datasets.