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:

• SimulST models must effectively handle lengthy and continuous speech inputs while maintaining high translation accuracy and low latency. 
• These models also face the challenge of “deciding” when to start translating without having access to the complete input, balancing the risk of premature outputs — leading to incomplete translations — against delays that increase latency, as both can negatively impact the user experience.
• Achieving the right balance between translation quality and latency is complex, as no single evaluation metric effectively addresses both aspects simultaneously.
• SimulST suffers from a lack of annotated training data, making it difficult to train models effectively and achieve optimal performance.

“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.

Source: https://slator.com/

Full article: https://slator.com/new-research-reviews-key-challenges-in-simultaneous-speech-translation/