Carlos Mullov


2022

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Effective combination of pretrained models - KIT@IWSLT2022
Ngoc-Quan Pham | Tuan Nam Nguyen | Thai-Binh Nguyen | Danni Liu | Carlos Mullov | Jan Niehues | Alexander Waibel
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

Pretrained models in acoustic and textual modalities can potentially improve speech translation for both Cascade and End-to-end approaches. In this evaluation, we aim at empirically looking for the answer by using the wav2vec, mBART50 and DeltaLM models to improve text and speech translation models. The experiments showed that the presence of these models together with an advanced audio segmentation method results in an improvement over the previous end-to-end system by up to 7 BLEU points. More importantly, the experiments showed that given enough data and modeling capacity to overcome the training difficulty, we can outperform even very competitive Cascade systems. In our experiments, this gap can be as large as 2.0 BLEU points, the same gap that the Cascade often led over the years.

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CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022
Peter Polák | Ngoc-Quan Pham | Tuan Nam Nguyen | Danni Liu | Carlos Mullov | Jan Niehues | Ondřej Bojar | Alexander Waibel
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

In this paper, we describe our submission to the Simultaneous Speech Translation at IWSLT 2022. We explore strategies to utilize an offline model in a simultaneous setting without the need to modify the original model. In our experiments, we show that our onlinization algorithm is almost on par with the offline setting while being 3x faster than offline in terms of latency on the test set. We also show that the onlinized offline model outperforms the best IWSLT2021 simultaneous system in medium and high latency regimes and is almost on par in the low latency regime. We make our system publicly available.