用离线语音模型实现多语种实时翻译，性能显著提升。
Simultaneous Translation with Offline Speech and LLM Models in CUNI Submission to IWSLT 2025
- 基于Whisper模型与对齐注意力策略，实现同步翻译
- 在英德、中日等语对上提升13-22 BLEU点
- 支持术语注入和上下文记忆，适合实际应用
本文介绍查理大学提交至IWSLT 2025同步语音翻译任务的系统。我们覆盖了全部四个语言对，采用直接或级联方法。系统核心为离线Whisper语音模型，结合最先进的对齐注意力（AlignAtt）同步策略，实现语音转录与翻译。通过提示工程注入领域术语并融入上下文信息进一步提升性能。级联系统还引入EuroLLM实现无界同步翻译。相比组织方基线，我们在捷克语→英语上提升2 BLEU点，英→德、英→中、英→日三个语对在开发集上提升13-22 BLEU点。此外，我们提出一种新的语音识别延迟评估指标。
原文摘要 · Abstract (English)
This paper describes Charles University submission to the Simultaneous Speech Translation Task of the IWSLT 2025. We cover all four language pairs with a direct or cascade approach. The backbone of our systems is the offline Whisper speech model, which we use for both translation and transcription in simultaneous mode with the state-of-the-art simultaneous policy AlignAtt. We further improve the performance by prompting to inject in-domain terminology, and we accommodate context. Our cascaded systems further use EuroLLM for unbounded simultaneous translation. Compared to the Organizers' baseline, our systems improve by 2 BLEU points on Czech to English and 13-22 BLEU points on English to German, Chinese and Japanese on the development sets. Additionally, we also propose a new enhanced measure of speech recognition latency.
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