您的即時同步語音轉文字翻譯系統有多「真實」？

How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?

December 24, 2024

作者: Sara Papi, Peter Polak, Ondřej Bojar, Dominik Macháček

cs.AI

摘要

同步語音轉文字翻譯（SimulST）將源語言的語音與演講者的演講同步地翻譯為目標語言文本，確保低延遲以提高使用者理解能力。儘管其應用於無限制語音，但大多數研究集中在人類預分段的語音上，簡化任務並忽略重要挑戰。這種狹隘焦點，再加上廣泛存在的術語不一致性，限制了研究成果應用於現實應用的可能性，最終阻礙了該領域的進展。我們對110篇論文進行了廣泛的文獻回顧，不僅揭示了當前研究中的關鍵問題，還為我們的主要貢獻奠定了基礎。我們1）定義了SimulST系統的步驟和核心組件，提出了標準術語和分類法；2）進行了對社區趨勢的深入分析；3）提出了具體的建議和未來方向，以彌合現有文獻中的差距，從評估框架到系統架構，推動該領域朝著更現實和有效的SimulST解決方案邁進。

English

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.