A method for real-time translation of online video subtitles in sports events

This study offers a fresh technique for translating subtitles in sports events, addressing the issues of real-time translation with improved accuracy and efficiency. Different from standard methods, which often result in delayed or inaccurate subtitles, the proposed method integrates advanced annotation techniques and machine learning algorithms to increase subtitle recognition and extraction. Annotation techniques in this study include systematically labeling spoken elements like commentary and dialogue, enabling accurate subtitle recognition and real-time adjustments in live sports broadcasts to ensure both accuracy and contextual relevance. These novel ideas allow for seamless adjustments to multiple language types, including the voices of commentators, off-site hosts, and athletes, while maintaining critical information within strict word count limits. Key improvements include faster processing times and increased translation precision, which are crucial for the dynamic environment of live sports broadcasts. The study builds on past studies in audiovisual translation, specifically tailoring its strategy to the unique demands of sports media. By emphasizing the importance of clear and contextually appropriate real-time subtitles, this research presents significant advancements over existing methods, providing valuable insights for future translation projects in sports and similar contexts. The results contribute to a more effective subtitle translation framework, enhancing the accessibility and viewing experience for audiences during live sports events.