title: Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning
abstract: The end-to-end speech translation (E2E-ST) model has gradually become a mainstream paradigm due to its low latency and less error propagation. However, it is non-trivial to train such a model well due to the task complexity and data scarcity. The speech-and-text modality differences result in the E2E-ST model performance usually inferior to the corresponding machine translation (MT) model. Based on the above observation, existing methods often use sharing mechanisms to carry out <italic xmlns:mm
journal: IEEE/ACM Transactions on Audio Speech and Language Processing