Mitigating Structural Noise in Low-Resource S2TT: An Optimized Cascaded Nepali-English Pipeline with Punctuation Restoration
arXiv:2602.21647v1 Announce Type: new Abstract: This paper presents and evaluates an optimized cascaded Nepali speech-to-English text translation (S2TT) system, focusing on mitigating structural noise introduced by Automatic Speech Recognition (ASR). 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
arXiv:2602.21647v1 Announce Type: new Abstract: This paper presents and evaluates an optimized cascaded Nepali speech-to-English text translation (S2TT) system, focusing on mitigating structural noise introduced by Automatic Speech Recognition (ASR). 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). 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.
Executive Summary
The article presents an optimized cascaded Nepali speech-to-English text translation (S2TT) system designed to mitigate structural noise introduced by Automatic Speech Recognition (ASR). The study establishes state-of-the-art ASR and Neural Machine Translation (NMT) components, demonstrating significant improvements in translation quality through the introduction of a Punctuation Restoration Module (PRM). The optimized pipeline achieved a 4.90 BLEU point gain over the baseline, validated by human assessments. The research highlights the critical role of punctuation restoration in enhancing the performance of S2TT systems for low-resource languages like Nepali.
Key Points
- ▸ State-of-the-art ASR and NMT components were developed for Nepali-English S2TT.
- ▸ Punctuation loss significantly degrades translation quality, causing a 20.7% relative BLEU drop.
- ▸ An intermediate Punctuation Restoration Module (PRM) was proposed and evaluated, leading to a 4.90 BLEU point gain.
- ▸ Human assessments confirmed the superior adequacy and fluency of the optimized pipeline.
- ▸ The study establishes an optimized baseline and critical architectural insights for cascaded speech translation systems.
Merits
Innovative Approach
The introduction of the Punctuation Restoration Module (PRM) is a novel and effective solution to mitigate structural noise in S2TT systems.
Empirical Validation
The study provides empirical evidence supporting the significance of punctuation restoration in improving translation quality.
State-of-the-Art Performance
The ASR and NMT components achieved state-of-the-art performance on benchmark datasets, demonstrating the robustness of the proposed methods.
Demerits
Limited Generalizability
The study focuses on Nepali-English S2TT, and the findings may not be directly applicable to other language pairs or low-resource languages.
Custom Dataset
The use of a custom dataset limits the comparability of the results with other studies that use standard benchmark datasets.
Human Assessment Bias
Human assessments, while valuable, can be subjective and may introduce bias into the evaluation of the system's performance.
Expert Commentary
The article presents a rigorous and well-reasoned approach to mitigating structural noise in low-resource S2TT systems. The introduction of the Punctuation Restoration Module (PRM) is a significant contribution to the field, as it addresses a critical bottleneck in the translation process. The empirical validation of the PRM's effectiveness, coupled with state-of-the-art performance on benchmark datasets, underscores the robustness of the proposed methods. However, the study's focus on Nepali-English S2TT and the use of a custom dataset limit the generalizability of the findings. Future research should explore the applicability of the PRM to other language pairs and low-resource languages to further validate its effectiveness. Additionally, the subjectivity inherent in human assessments warrants caution in interpreting the results. Overall, the study provides valuable insights and establishes an optimized baseline for developing cascaded speech translation systems for low-resource languages.
Recommendations
- ✓ Future research should investigate the applicability of the Punctuation Restoration Module (PRM) to other language pairs and low-resource languages to assess its generalizability.
- ✓ Standard benchmark datasets should be used in addition to custom datasets to enhance the comparability of results with other studies in the field.
