On August 20, 2024, researchers from Northeastern University, Jinan University, Harbin Engineering University, and NiuTrans Research published a comprehensive overview of the challenges and advancements in the field of simultaneous speech translation (SimulST). The authors describe SimulST as “especially beneficial in scenarios that require fast and smooth communication,” such as live conversations and voice conferencing. Given its critical role in real-time communication, the field has received significant attention and made notable progress in recent years. However, it remains a “demanding task,” according to the authors, who identified and outlined four key challenges that complicate SimulST: “These factors collectively contribute to the intricate nature of the SimulST task,” the authors noted. While previous studies have proposed solutions to these challenges, a comprehensive overview summarizing these practices has been missing. With this paper, the authors aim to fill that gap by providing “a more complete and comprehensive introduction to SimulST.” “Through our exploration of these challenges and the proposed solutions, we aim to provide valuable insights into the current landscape of SimulST research and suggest promising directions for future exploration,” they said. To effectively manage the processing of lengthy, continuous speech in real-time, SimulST systems should rely on robust segmentation strategies that allow for the generation of partial translations without waiting for the speaker to complete their input. Given that spoken language often lacks clear boundaries, accurate segmentation becomes a complex task. According to the authors, the following method can be used to address this challenge: To determine the optimal moments for translation, SimulST systems should make timely decisions about when to begin translating without having access to the entire input. Simultaneous Read-Write (R-W) policies are essential for generating partial translations while processing streaming speech, ensuring a natural conversational flow. According to the authors, the following R-W strategies are recommended: Navigating the trade-off between translation quality and latency is crucial for SimulST. Employing diverse evaluation metrics provides unique insights into system behavior — some metrics focus on the accuracy of the translations (quality-related), while others measure the speed at which translations are produced (latency-related) — allowing researchers to optimize performance while considering both speed and accuracy. To address the scarcity of annotated training data, there are two effective strategies, according to the authors. The first, data augmentation, involves techniques to artificially expand training datasets by generating additional examples from existing data or using synthetic data to improve learning. The second, multi-task learning, enables models to learn from multiple related tasks simultaneously, leveraging shared information across tasks. This approach can be particularly beneficial in scenarios where data for one task is limited, as it can enhance the model’s ability to generalize from related tasks. Looking ahead, the authors identify two “promising directions” for the field: multilingual SimulST and integration with large language models (LLMs). Multilingual SimulST enables real-time translation of speech inputs into multiple languages and holds “significant potential” to facilitate communication and collaboration in multilingual environments, according to the authors. Integrating LLMs into SimulST systems enhances their ability to accurately understand speech inputs, handle contextual dependencies, and generate fluent translations. The researchers anticipate that combining LLMs with SimulST will improve the performance and applicability of streaming speech translation systems, meeting diverse user needs in real-time scenarios. Authors: Xiaoqian Liu, Guoqiang Hu, Yangfan Du, Erfeng He, Yingfeng Luo, Chen Xu, Tong Xiao, Jingbo Zhu