NAIST Offline Speech Translation System for IWSLT 2025 (2025)

Abstract: This paper presents NAIST’s submission to the offline speech translation task of the IWSLT 2025 evaluation campaign, focusing on English-to-German and English-to-Chinese translation. We implemented both cascade and end-to-end frameworks using various components. For the cascade approach, we used Whisper and SALMONN as automatic speech recognition systems, each paired with Qwen2.5 large language model (LLM) for translation. In the end-to-end setting, we used SALMONN as speech translation and also built a custom model combining the Whisper encoder, DeCo projector, and Qwen2.5 LLM. To further leverage the large language model capabilities, we experimented with different prompting strategies. Additionally, since long speech inputs are segmented for processing, we applied hypothesis combination techniques to generate the final translation output. Our results show that combining Whisper and LLMs can yield strong translation performance, even without further fine-tuning in the cascade setup. Moreover, our proposed end-to-end architecture achieved competitive results, despite being trained on significantly less data compared to SALMONN. Finally, we decided to use both SALMONN as an end-to-end speech translation model and our proposed end-to-end model for our IWSLT 2025 submission for both language pairs.

## introduction spoken language translation (slt) refers to the process of automatically converting spoken audio into written text in another language. within the iwslt shared task, the offline speech translation task stands out as one of the longest-running tracks. its goal is to offer a consistent evaluation setting for speech translation, without the timing and structural limitations typically associated with other tasks-such as real-time constraints in simultaneous interpretation, space restrictions in subti-tling, duration matching in dubbing, or the challenges posed by limited data in low-resource language scenarios. in the 2025 edition of the offline speech translation task @xcite , three translation directions are included: english to german, chinese, and arabic. this year's challenge places particular emphasis on tackling more practical translation scenarios, such as content from tv shows, academic talks, business news, and speech with diverse accents. our team at naist is participating in the english-german and english-chinese tracks. unfortunately, due to limited preparation time, we were not able to take part in the english-arabic track. to address these translation tasks, we explore two widely used slt frameworks: the cascade and end-to-end approaches. the cascade method separates the process into two stages-first transcribing speech using automatic speech recognition (asr), followed by translating the transcription with a machine translation (mt) system. in contrast, the end-to-end approach generates translations directly from the speech input, integrating both steps into a single model. while the cascade framework benefits from modularity and reuse of existing asr and mt models, it is susceptible to error propagation. end-to-end systems can mitigate such issues, but they often struggle with data scarcity, as large-scale parallel speech-to-text corpora remain limited. in particular, we implemented both frameworks using a range of components. for the cascade approach, we explored two asr systems -whisper foot_0 @xcite and salmonn 2 @xcite -each paired with the qwen2.5 foot_2 @xcite llm for machine translation. in the end-to-end setting, we treated salmonn as a unified speech translation system. additionally, we developed a custom end-to-end model that integrates the whisper encoder, a deco @xcite projection module, and qwen2.5 as the decoder. to further investigate the capabilities of large language models, we conducted experiments with different prompting strategies and analyzed their impact on translation performance. since long speech inputs are segmented and processed separately, we also explore hypothesis combination strategies to produce the final translation output. ## system description as outlined earlier, this work explores both cascade and end-to-end approaches to speech translation, utilizing a range of components including whisper, salmonn, qwen2.5, and others. in the following sections, we first describe the model architectures of these components in section 2.1. we then explain our methods for applying zero-shot, few-shot learning, or fine-tuning to both the cascaded and end-to-end speech translation settings in sections 2.2 and 2.3, respectively. ## conclusion this paper describes naist's submission to the iwslt 2025 offline speech translation task, focusing on english-to-german and english-to-chinese translation. we found that using whisper as the asr combined with qwen2.5 llm as the mt in a zero-shot setting was already capable of producing good translations. furthermore, in the zero-shot setting, translation quality for long audio was better when the transcriptions of individual segments were combined first and then translated together, compared to translating each segment individually and combining the translations afterward. however, few-shot learning yielded better results in the latter case. fine-tuning the salmonn model further improved its translation quality. additionally, our custom end-to-end model demonstrated competitive performance with salmonn, despite being trained on significantly less data. finally, we observed that both qwen2.5 and salmonn performed better on english-to-chinese translation than on english-to-german.