通过跨模式学习和同步优化来增强英语口头翻译
1Anhui Wenda University of Information Engineering, Hefei, Anhui, China.
PloS one
|August 18, 2025
概括
这项研究引入了一种新的英语口译融合模型,提高同步翻译的质量和效率. 与现有方法相比,该模型显著提高了准确性和同步速度.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 由于发音变化和快速的演讲,口语英语翻译面临着挑战.
- 目前的同步翻译方法在实时口语环境中缺乏有效性.
研究的目的:
- 提高同步英语口语翻译的质量和效率.
- 探索跨模式语义理解和同步增强的整合.
主要方法:
- 实施了一种跨模式的翻译场景.
- 使用来自变压器的双向编码器表示 (BERT) 来将文本序列与语音特征合并.
- 优化了变压器的自我注意力机制,用于上下文意识和动态时间扭曲 (DTW) 以实现实时同步.
主要成果:
- 与现有模型相比,实现了9.3%和26.9%的平均双语评估研究 (BLEU) 得分.
- 显示了比其他模型更快的同步速度17.9%和16.8%.
- 拟议的融合模型显著提高了翻译流性和准确性.
结论:
- 融合模型在跨模式翻译中结合了上下文意识和注意力机制,提高了英语口头翻译的质量.
- 这种方法为同步口语英语翻译提供了一个新的解决方案.
- 这些发现突显了先进的人工智能模型在克服实时翻译障碍方面的潜力.
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