Thai-Binh Nguyen

Also published as: Thai Binh Nguyen, Thai Binh Nguyen


pdf bib
KIT’s Multilingual Speech Translation System for IWSLT 2023
Danni Liu | Thai Binh Nguyen | Sai Koneru | Enes Yavuz Ugan | Ngoc-Quan Pham | Tuan Nam Nguyen | Tu Anh Dinh | Carlos Mullov | Alexander Waibel | Jan Niehues
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

Many existing speech translation benchmarks focus on native-English speech in high-quality recording conditions, which often do not match the conditions in real-life use-cases. In this paper, we describe our speech translation system for the multilingual track of IWSLT 2023, which focuses on the translation of scientific conference talks. The test condition features accented input speech and terminology-dense contents. The tasks requires translation into 10 languages of varying amounts of resources. In absence of training data from the target domain, we use a retrieval-based approach (kNN-MT) for effective adaptation (+0.8 BLEU for speech translation). We also use adapters to easily integrate incremental training data from data augmentation, and show that it matches the performance of re-training. We observe that cascaded systems are more easily adaptable towards specific target domains, due to their separate modules. Our cascaded speech system outperforms its end-to-end counterpart on scientific talk translation, although their performance remains similar on TED talks.

pdf bib
End-to-End Evaluation for Low-Latency Simultaneous Speech Translation
Christian Huber | Tu Anh Dinh | Carlos Mullov | Ngoc-Quan Pham | Thai Binh Nguyen | Fabian Retkowski | Stefan Constantin | Enes Ugan | Danni Liu | Zhaolin Li | Sai Koneru | Jan Niehues | Alexander Waibel
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

The challenge of low-latency speech translation has recently draw significant interest in the research community as shown by several publications and shared tasks. Therefore, it is essential to evaluate these different approaches in realistic scenarios. However, currently only specific aspects of the systems are evaluated and often it is not possible to compare different approaches. In this work, we propose the first framework to perform and evaluate the various aspects of low-latency speech translation under realistic conditions. The evaluation is carried out in an end-to-end fashion. This includes the segmentation of the audio as well as the run-time of the different components. Secondly, we compare different approaches to low-latency speech translation using this framework. We evaluate models with the option to revise the output as well as methods with fixed output. Furthermore, we directly compare state-of-the-art cascaded as well as end-to-end systems. Finally, the framework allows to automatically evaluate the translation quality as well as latency and also provides a web interface to show the low-latency model outputs to the user.


pdf bib
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.