发表机构
Oregon State University（俄勒冈州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出首个同声手语到手语翻译系统，采用两种wait-k机制并引入计算感知延迟度量ca-Stream-AL，在六个方向上实现38%延迟降低，同时保持较小的质量损失。
AI 中文摘要
聋人和重听（DHH）手语使用者目前无法在不同手语之间进行实时对话：现有的手语到手语翻译系统是离线运行的，需要在发出任何目标手语之前获取完整的源片段。实时用例——例如广播口译和双向视频通话——则要求在手语者仍在打手语时进行同步输出。我们提出了据我们所知的第一个同声手语到手语（S2S）翻译系统，具有两种wait-k机制：直接应用于全句模型的测试时wait-k推理，以及通过随机多路径监督训练的wait-k模型。我们还引入了ca-Stream-AL，一种用于流式输出的计算感知延迟度量。在六个S2S方向上，分别在较小的人工验证测试集和较大的合成S2S语料库上取平均，我们的流式系统实现了38%的ca-Stream-AL降低，同时与全句基线相比，DTW-PA-MPJPE增加保持在9%以内，BLEU-4下降2.1。一个词序案例研究探讨了流式模型如何处理不同手语之间的词序不匹配——这是同声翻译的一个结果。
英文摘要
Deaf and hard-of-hearing (DHH) signers cannot converse in real time across different sign languages today: existing sign-to-sign translation systems run offline, requiring the full source clip before any target sign is emitted. Live use cases - e.g. broadcast interpretation and two-way video calls - instead demand simultaneous output, while the source signer is still signing. We present, to our knowledge, the first simultaneous sign-to-sign (S2S) translation system, with two wait-k regimes: test-time wait-k inference applied directly to a full-sentence model, and a trained wait-k model via stochastic multi-path supervision. We further introduce ca-Stream-AL, a computation-aware latency metric for streaming output. Averaged across six S2S directions on both a smaller human-verified test set and a larger synthetic S2S corpus, our streaming system achieves a 38% ca-Stream-AL reduction while staying within a 9% DTW-PA-MPJPE increase and a 2.1 BLEU-4 drop compared to the full-sentence baseline. A word-order case study probes how the streaming model handles word order mismatch between different sign languages - a consequence of simultaneous translation.
