Simultaneous Speech Translation

Simultaneous speech translation (SST) aims to generate real-time translations of spoken language, posing significant challenges in balancing translation quality with low latency. Current research focuses on improving the efficiency and accuracy of end-to-end models, often employing transformer architectures with techniques like blockwise processing, adaptive decision policies (e.g., integrate-and-fire mechanisms), and novel training strategies to mitigate gradient conflicts and optimize the quality-latency trade-off. These advancements are crucial for enhancing human-computer interaction and cross-lingual communication in various applications, such as real-time subtitling, interpreting services, and multilingual meetings.