NYA ’s Offline Speech Translation System for IWSLT 2025 (2025)

Abstract: This paper reports NYA’s submissions to the IWSLT 2025 Offline Speech Translation (ST) task. The task includes three translation directions: English to Chinese, German, and Arabic. In detail, we adopt a cascaded speech translation architecture comprising automatic speech recognition (ASR) and machine translation (MT) components to participate in the unconstrained training track. For the ASR model, we use the Whisper medium model. For the neural machine translation (NMT) model, the wider and deeper Transformer is adopted as the backbone model. Building upon last year’s work, we implement multiple techniques and strategies such as data augmentation, domain adaptation, and model ensemble to improve the translation quality of the NMT model. In addition, we adopt X-ALMA as the foundational LLM-based MT model, with domain-specific supervised fine-tuning applied to train and optimize our LLM-based MT model. Finally, by employing COMET-based Minimum Bayes Risk decoding to integrate and select translation candidates from both NMT and LLM-based MT systems, the translation quality of our ST system is significantly improved, and competitive results are obtained on the evaluation set.

## introduction the offline speech translation (st) task converts source audio into target text. currently, two primary approaches dominate the st field: the cascaded system and the end-to-end (e2e) system. the traditional cascade system @xcite decouples the st task into an automatic speech recognition (asr) and a machine translation (mt) task. the source speech is first transcribed into text in the source language, which is then translated into text in the target language using a neural machine translation (nmt) model. however, it often leads to higher architectural complexity and error propagation @xcite , affecting subsequent mt tasks. to alleviate this problem, the end-to-end st architecture @xcite is proposed. the e2e st system employs a single neural network to directly map source-language audio to target-language text, bypassing intermediate symbolic representations. for end-to-end st architectures, a key limitation is the scarcity of parallel speech-text data. in contrast, the widespread availability of large-scale asr and mt datasets facilitates the development of high-precision asr and mt systems through comprehensive training. therefore, the cascaded st system typically outperforms the e2e st system @xcite @xcite . thus, we choose the cascaded st scheme consisting of asr and mt systems for the task. the main architecture of the traditional nmt model is the encoder-decoder. recently, large language models (llms) based on decoder-only architectures have demonstrated remarkable performance across various natural language processing (nlp) tasks. in the mt task, only the most advanced llms like gpt-4 @xcite can match the performance of supervised learning-based encoder-decoder state-ofthe-art (sota) models such as nllb @xcite ), yet their effectiveness still falls short of expectations in low-resource languages and specialized domains. therefore, many studies @xcite @xcite are focused on applying llms to smaller-scale models, broader language coverage, and more diverse application scenarios in machine translation, demonstrating significant advancements in the field. for example, x-alma @xcite is one of the top-performing translation models built on llms, capable of matching or even surpassing wmt winners and gpt-4 in some language pairs and scenarios. therefore, unlike in previous work, we implement both nmt and llm-based mt approaches and investigate their combination to achieve improved translation performance. we participate in the unconstrained training track of the offline speech translation task. and the whisper @xcite medium model is directly employed for the asr system in the source language. we also explore audio segmentation methods, such as supervised hybrid audio segmentation (shas) @xcite , to segment the source audio for better st results. in the mt task, we widely collect a large amount of parallel data and monolingual data from various data sources. for the nmt system, we use the transformer architecture @xcite as the backbone model and implement multiple optimization techniques and strategies such as back translation (bt) @xcite , forward translation (ft), domain adaptation (da), and ensemble @xcite to improve the translation quality of the nmt model. for the llm-based mt system, we use x-alma as the foundational model and adopt supervised fine-tuning (sft) to train and optimize the llm-based mt model. subsequently, we adopt minimum bayes risk (mbr) @xcite decoding to select the translation candidates from both nmt and llm-based mt systems and obtain significant improvements in translation quality. ## experiments and results all nmt models are implemented using the opensource fairseq toolkit @xcite . for llm fine-tuning, we utilize the open-source alma toolkit foot_3 @xcite . we evaluate the performance of mt models using case-sensitive sacre- ## conclusion this paper presents our submission to the iwslt 2025 offline speech translation task. for the unconstrained track, we adopt a cascaded speech translation architecture consisting of the asr and mt systems. for the asr system, we directly employ the open-source whisper medium model, which has shown outstanding performance and strong robustness across various scenarios for english speech recognition tasks. for the mt system, we investigate both nmt-based and llm-based approaches and explore optimization strategies including data augmentation, domain adaptation, mbr decoding, and model ensemble. experimental results demonstrate that integrating nmt with llm-based mt models while applying these techniques yields significant performance improvements. our final system achieves comet scores of 0.8454, 0.7736, and 0.8689 for @xmath0, @xmath1, and @xmath2, respectively.