How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?
Sara Papi, Peter Polak, Ondřej Bojar, Dominik Macháček
2024-12-26
Summary
This paper talks about the challenges and advancements in simultaneous speech-to-text translation (SimulST), which translates spoken language into written text in real-time, and proposes solutions to improve its effectiveness.
What's the problem?
While SimulST is designed to provide quick translations of spoken language, most research has focused on simpler tasks, like translating pre-segmented speech. This narrow focus overlooks the complexities of translating natural, flowing speech, which can lead to misunderstandings and inaccuracies. Additionally, inconsistent terminology in the field makes it hard to apply research findings to real-world situations.
What's the solution?
The authors conducted a thorough review of 110 research papers to identify these issues. They propose a standardized framework for SimulST that defines its core components and terminology. They also analyze trends in the field and provide recommendations for future research, including better evaluation methods and system designs that can handle the complexities of real-time translation more effectively.
Why it matters?
This research is important because it aims to improve how we translate spoken language into text, making communication smoother and more accurate in real-time situations like conferences or conversations between people who speak different languages. By addressing the gaps in current research and providing clear guidelines, this work could help advance the technology used in translation systems, leading to better understanding and interaction across languages.
Abstract
Simultaneous speech-to-text translation (SimulST) translates source-language speech into target-language text concurrently with the speaker's speech, ensuring low latency for better user comprehension. Despite its intended application to unbounded speech, most research has focused on human pre-segmented speech, simplifying the task and overlooking significant challenges. This narrow focus, coupled with widespread terminological inconsistencies, is limiting the applicability of research outcomes to real-world applications, ultimately hindering progress in the field. Our extensive literature review of 110 papers not only reveals these critical issues in current research but also serves as the foundation for our key contributions. We 1) define the steps and core components of a SimulST system, proposing a standardized terminology and taxonomy; 2) conduct a thorough analysis of community trends, and 3) offer concrete recommendations and future directions to bridge the gaps in existing literature, from evaluation frameworks to system architectures, for advancing the field towards more realistic and effective SimulST solutions.