分析同步语音翻译延迟波动，提出优化策略提升稳定性。
What does it take to get state of the art in simultaneous speech-to-speech translation?
- 通过调整输入与参数减少幻觉引发的延迟突增。
- 实验验证可显著改善模型延迟表现。
- 适合关注实时语音翻译系统优化的研究者。
本文深入分析了同步语音到语音翻译模型性能中的延迟特性，尤其关注由幻觉引发的延迟突增现象。通过系统性地测试不同输入参数和条件，我们提出了降低延迟突增并提升整体性能的方法。研究结果表明，通过精心管理输入数据并进行有针对性的参数调整，能够显著改善语音翻译模型的延迟行为。该工作为构建更稳定、高效的实时语音翻译系统提供了实用指导。
原文摘要 · Abstract (English)
This paper presents an in-depth analysis of the latency characteristics observed in simultaneous speech-to-speech model's performance, particularly focusing on hallucination-induced latency spikes. By systematically experimenting with various input parameters and conditions, we propose methods to minimize latency spikes and improve overall performance. The findings suggest that a combination of careful input management and strategic parameter adjustments can significantly enhance speech-to-speech model's latency behavior.
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