针对尼泊尔语语音翻译中的标点丢失问题，提出优化级联管道提升翻译质量。
Mitigating Structural Noise in Low-Resource S2TT: An Optimized Cascaded Nepali-English Pipeline with Punctuation Restoration
- 在自动语音识别后加入标点恢复模块，修复结构噪声。
- 标点恢复使翻译BLEU提升4.90点，相对下降20.7%的损失被有效缓解。
- 适合低资源语言语音翻译系统开发者参考，尤其关注结构完整性。
针对低资源语言的级联语音到文本翻译系统易受结构噪声影响，尤其是自动语音识别阶段标点丢失问题。本研究以尼泊尔语-英语翻译为例，构建高性能的ASR与NMT组件：基于Wav2Vec2-XLS-R-300m模型在OpenSLR-54上实现2.72%的词错误率（CER），多阶段微调的MarianMT模型在FLORES-200上达到28.32的BLEU得分。实证表明，未加标点的ASR输出导致翻译质量显著下降，在FLORES基准上造成20.7%的相对BLEU下降。为此，提出并评估了中间标点恢复模块（PRM）。最终在自建数据集上测试三种配置，最优方案直接对ASR输出应用PRM，相较直接端到端的ASR-to-NMT基线（BLEU 31.48）提升4.90点至36.38。人工评估确认其在充分性（3.673）和流畅性（3.804）上更优，且评分者间一致性（Krippendorff's α ≥ 0.723）良好。结果验证，针对性的标点恢复是缓解尼泊尔语语音翻译中结构噪声最有效的手段，为类似低资源语言的级联翻译系统提供关键架构启示。
原文摘要 · Abstract (English)
Cascaded speech-to-text translation (S2TT) systems for low-resource languages can suffer from structural noise, particularly the loss of punctuation during the Automatic Speech Recognition (ASR) phase. This research investigates the impact of such noise on Nepali-to-English translation and proposes an optimized pipeline to mitigate quality degradation. We first establish highly proficient ASR and NMT components: a Wav2Vec2-XLS-R-300m model achieved a state-of-the-art 2.72% CER on OpenSLR-54, and a multi-stage fine-tuned MarianMT model reached a 28.32 BLEU score on the FLORES-200 benchmark. We empirically investigate the influence of punctuation loss, demonstrating that unpunctuated ASR output significantly degrades translation quality, causing a massive 20.7% relative BLEU drop on the FLORES benchmark. To overcome this, we propose and evaluate an intermediate Punctuation Restoration Module (PRM). The final S2TT pipeline was tested across three configurations on a custom dataset. The optimal configuration, which applied the PRM directly to ASR output, achieved a 4.90 BLEU point gain over the direct ASR-to-NMT baseline (BLEU 36.38 vs. 31.48). This improvement was validated by human assessment, which confirmed the optimized pipeline's superior Adequacy (3.673) and Fluency (3.804) with inter-rater reliability (Krippendorff's $α {\geq}$ 0.723). This work validates that targeted punctuation restoration is the most effective intervention for mitigating structural noise in the Nepali S2TT pipeline. It establishes an optimized baseline and demonstrates a critical architectural insight for developing cascaded speech translation systems for similar low-resource languages.
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