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pro vyhledávání: '"Mullov, Carlos"'
Autor:
Dinh, Tu Anh, Mullov, Carlos, Bärmann, Leonard, Li, Zhaolin, Liu, Danni, Reiß, Simon, Lee, Jueun, Lerzer, Nathan, Ternava, Fabian, Gao, Jianfeng, Waibel, Alexander, Asfour, Tamim, Beigl, Michael, Stiefelhagen, Rainer, Dachsbacher, Carsten, Böhm, Klemens, Niehues, Jan
With the rapid development of Large Language Models (LLMs), it is crucial to have benchmarks which can evaluate the ability of LLMs on different domains. One common use of LLMs is performing tasks on scientific topics, such as writing algorithms, que
Externí odkaz:
http://arxiv.org/abs/2406.10421
Autor:
Huber, Christian, Dinh, Tu Anh, Mullov, Carlos, Pham, Ngoc Quan, Nguyen, Thai Binh, Retkowski, Fabian, Constantin, Stefan, Ugan, Enes Yavuz, Liu, Danni, Li, Zhaolin, Koneru, Sai, Niehues, Jan, Waibel, Alexander
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 scenari
Externí odkaz:
http://arxiv.org/abs/2308.03415
Autor:
Liu, Danni, Nguyen, Thai Binh, Koneru, Sai, Ugan, Enes Yavuz, Pham, Ngoc-Quan, Nguyen, Tuan-Nam, Dinh, Tu Anh, Mullov, Carlos, Waibel, Alexander, Niehues, Jan
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 multilin
Externí odkaz:
http://arxiv.org/abs/2306.05320
Autor:
Waibel, Alexander, Behr, Moritz, Eyiokur, Fevziye Irem, Yaman, Dogucan, Nguyen, Tuan-Nam, Mullov, Carlos, Demirtas, Mehmet Arif, Kantarcı, Alperen, Constantin, Stefan, Ekenel, Hazım Kemal
In this paper, we propose a neural end-to-end system for voice preserving, lip-synchronous translation of videos. The system is designed to combine multiple component models and produces a video of the original speaker speaking in the target language
Externí odkaz:
http://arxiv.org/abs/2206.04523
Autor:
Polák, Peter, Ngoc, Ngoc-Quan, Nguyen, Tuan-Nam, Liu, Danni, Mullov, Carlos, Niehues, Jan, Bojar, Ondřej, Waibel, Alexander
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 tha
Externí odkaz:
http://arxiv.org/abs/2204.06028
In this work we look into adding a new language to a multilingual NMT system in an unsupervised fashion. Under the utilization of pre-trained cross-lingual word embeddings we seek to exploit a language independent multilingual sentence representation
Externí odkaz:
http://arxiv.org/abs/2103.06689
Autor:
Pham, Ngoc-Quan, Nguyen, Tuan Nam, Nguyen, Thai-Binh, Liu, Danni, Mullov, Carlos, Niehues, Jan, Waibel, Alexander
Publikováno v:
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022), 190-197
STARTPAGE=190;ENDPAGE=197;TITLE=Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)
STARTPAGE=190;ENDPAGE=197;TITLE=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 mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f0c639e12e9f556b0f76a1c1257624e
https://doi.org/10.18653/v1/2022.iwslt-1.14
https://doi.org/10.18653/v1/2022.iwslt-1.14