MLLP - VRAIN UPV system for the IWSLT 2025 Simultaneous Speech Translation Translation task (2025)

Abstract: This work describes the participation of the MLLP-VRAIN research group in the shared task of the IWSLT 2025 Simultaneous Speech Translation track. Our submission addresses the unique challenges of real-time translation of long-form speech by developing a modular cascade system that adapts strong pre-trained models to streaming scenarios. We combine Whisper Large-V3-Turbo for ASR with the multilingual NLLB-3.3B model for MT, implementing lightweight adaptation techniques rather than training new end-to-end models from scratch. Our approach employs document-level adaptation with prefix training to enhance the MT model’s ability to handle incomplete inputs, while incorporating adaptive emission policies including a wait-k strategy and RALCP for managing the translation stream. Specialized buffer management techniques and segmentation strategies ensure coherent translations across long audio sequences. Experimental results on the ACL60/60 dataset demonstrate that our system achieves a favorable balance between translation quality and latency, with a BLEU score of 31.96 and non-computational-aware StreamLAAL latency of 2.94 seconds. Our final model achieves a preliminary score on the official test set (IWSLT25Instruct) of 29.8 BLEU. Our work demonstrates that carefully adapted pre-trained components can create effective simultaneous translation systems for long-form content without requiring extensive in-domain parallel data or specialized end-to-end training.

## introduction in this paper we describe the participation of the mllp-vrain research group in the shared tasks of the 22th international conference on spoken language translation (iwslt) @xcite . we participated on the simultaneous speech translation (simulst) task in the english to german direction. compared to other years, two aspects were changed in the shared task which guided the construction of our system: the usage of pretrained open weight models and the evaluation of long-form audio. our participation this year was an attempt of creating a production ready model based on the minimal adaptation of offline asr and mt @xcite . recent years have seen a rise in the usage of end-to-end approaches foot_0 which, in theory, can offer a better integration and can avoid error compounding between asr and mt components. however, they typically require large quantities of parallel speech-to-text translation data, which is often scarce and costly to obtain. cascade systems, on the other hand, while they are more data-efficient due to the abundance of separate asr and mt training resources, may suffer from error propagation and lack of joint optimization. nevertheless, recent shared tasks and evaluation campaigns continue to show that cascade systems generally achieve superior performance over current end-to-end alternatives @xcite . as such, we model our system based on the cascaded approach, in which we take a special keen interest due to its inherent modularity and easier reuse of strong pre-trained components. figure 1 shows the overall architecture of our system. ## evaluation for our baselines, we searched for any other public simultaneous speech translation systems capable of long form translation. however, we only found the system described in @xcite available, and in this case, we observed that the system had strong hallucinations that ended up in an unrecoverable state for long enough audios. due to this, we instead build a "naive" baseline for our system which make use of the plain translation system without any additional training. for controlling the history buffers, we simply remove from the source and target text history buffers after a maximum number of words is reached in each buffer. in practice, we surpassingly found that while the system will end up with slight mismatches between the source and target stream at the head position, systems are still able to work in a streaming scenario. we additionally add the offline inference of the model before and after fine-tuning and the best baseline from the iwslt organizers that achieved a similar latency-quality tradeoff matching our own. 7 . best hyperparameters with the optimal quality/latency are shown in appendix a. in terms of baselines, we can see that our naive baseline slight beats the organizers baseline in the selected quality-latency range. when comparing our baseline and adapted model, we can see a considerable increase in both quality and reduction model m mdn p90 p95 p99 max to better study the latency of our system, an observation that can be made in this table is that of the considerable increase of latency for the worst cases between the nca and ca metrics. after investigating, we discovered the temperature fallback mechanism of whisper to seem to cause this phenomena, resulting in some rare cases where latency spikes occur and can only be observed when taking computational costs into account. despite this, we found that in practice the performance is really poor with this feature disabled. in general, we observed that the asr emission policy highly influenced the system performance, with hyperparameter changes on the mt system having less of an overall impact. regarding the official test set (iwslt25instruct), preliminary results by the organizers indicate that our model achieved a final score of 29.8 bleu. ## conclusions in this paper we described our simulst system for the iwslt 2025 simultaneous speech translation task. preliminary results show that our cascaded based system using whisper and nllb showed a good performance and achieved a good balance between translation quality and latency. we see how through adaptive policies and very computationally cheap adaptation a long-form speech simulst system can be created from offline models. future work could be expand to see the robustness of this methodology, such as the usage of synthetic document level bitext data @xcite or speech data. investigating more robust adaptive latency policies or techniques which better optimize @xcite while preserving the benefits of the cascaded approach could further greatly enhance the system performance. also, a gap still exists compared to offline translation, which should be further explore d more detail. additionally, the usage of llms to serve as all in one transcriber, translator and re-scorer in a cascaded pipeline along their robustness and usage of long context shows promising results for their usage in long-form speech translation if computational costs can be taken into account.