This paper describes the NVIDIA NeMo team’s submission to the IWSLT 2026 Simultaneous Speech Translation (SimulST) tracks. We use a cascaded architecture combining a dual-mode Unified ASR Transducer model with a multilingual Large Language Model (LLM). The ASR is trained to deliver stable transcriptions across wide range of latencies, providing a reliable foundation for high-quality LLM translation. Our submission participates in the English–German, English–Italian, and English–Chinese tasks, in both standard and contextualized settings, as well as the Czech–English standard track, covering both low- and high-latency scenarios. We further analyze how ASR and LLM design choices affect the system’s overall latency and translation quality.

This paper provides an overview of NVIDIA NeMo’s speech translation systems for the IWSLT 2023 Offline Speech Translation Task. This year, we focused on end-to-end system which capitalizes on pre-trained models and synthetic data to mitigate the problem of direct speech translation data scarcity. When trained on IWSLT 2022 constrained data, our best En->De end-to-end model achieves the average score of 31 BLEU on 7 test sets from IWSLT 2010-2020 which improves over our last year cascade (28.4) and end-to-end (25.7) submissions. When trained on IWSLT 2023 constrained data, the average score drops to 29.5 BLEU.