no code implementations • EMNLP (IWSLT) 2019 • Jan Niehues, Rolando Cattoni, Sebastian Stüker, Matteo Negri, Marco Turchi, Thanh-Le Ha, Elizabeth Salesky, Ramon Sanabria, Loic Barrault, Lucia Specia, Marcello Federico
The IWSLT 2019 evaluation campaign featured three tasks: speech translation of (i) TED talks and (ii) How2 instructional videos from English into German and Portuguese, and (iii) text translation of TED talks from English into Czech.
no code implementations • EMNLP (IWSLT) 2019 • Ngoc-Quan Pham, Thai-Son Nguyen, Thanh-Le Ha, Juan Hussain, Felix Schneider, Jan Niehues, Sebastian Stüker, Alexander Waibel
This paper describes KIT’s submission to the IWSLT 2019 Speech Translation task on two sub-tasks corresponding to two different datasets.
no code implementations • IWSLT 2016 • Mauro Cettolo, Jan Niehues, Sebastian Stüker, Luisa Bentivogli, Rolando Cattoni, Marcello Federico
The IWSLT 2016 Evaluation Campaign featured two tasks: the translation of talks and the translation of video conference conversations.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • IWSLT 2016 • Markus Müller, Sebastian Stüker, Alex Waibel
For system training, we use additional data from French, German and Turkish.
no code implementations • IWSLT 2016 • Thai-Son Nguyen, Markus Müller, Matthias Sperber, Thomas Zenkel, Kevin Kilgour, Sebastian Stüker, Alex Waibel
For the English TED task, our best combination system has a WER of 7. 8% on the development set while our other combinations gained 21. 8% and 28. 7% WERs for the English and German MSLT tasks.
no code implementations • IWSLT (ACL) 2022 • Antonios Anastasopoulos, Loïc Barrault, Luisa Bentivogli, Marcely Zanon Boito, Ondřej Bojar, Roldano Cattoni, Anna Currey, Georgiana Dinu, Kevin Duh, Maha Elbayad, Clara Emmanuel, Yannick Estève, Marcello Federico, Christian Federmann, Souhir Gahbiche, Hongyu Gong, Roman Grundkiewicz, Barry Haddow, Benjamin Hsu, Dávid Javorský, Vĕra Kloudová, Surafel Lakew, Xutai Ma, Prashant Mathur, Paul McNamee, Kenton Murray, Maria Nǎdejde, Satoshi Nakamura, Matteo Negri, Jan Niehues, Xing Niu, John Ortega, Juan Pino, Elizabeth Salesky, Jiatong Shi, Matthias Sperber, Sebastian Stüker, Katsuhito Sudoh, Marco Turchi, Yogesh Virkar, Alexander Waibel, Changhan Wang, Shinji Watanabe
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
no code implementations • IWSLT (EMNLP) 2018 • Jan Niehues, Rolando Cattoni, Sebastian Stüker, Mauro Cettolo, Marco Turchi, Marcello Federico
The International Workshop of Spoken Language Translation (IWSLT) 2018 Evaluation Campaign featured two tasks: low-resource machine translation and speech translation.
no code implementations • IWSLT (EMNLP) 2018 • Matthias Sperber, Ngoc-Quan Pham, Thai-Son Nguyen, Jan Niehues, Markus Müller, Thanh-Le Ha, Sebastian Stüker, Alex Waibel
The baseline system is a cascade of an ASR system, a system to segment the ASR output and a neural machine translation system.
no code implementations • ACL (IWSLT) 2021 • Antonios Anastasopoulos, Ondřej Bojar, Jacob Bremerman, Roldano Cattoni, Maha Elbayad, Marcello Federico, Xutai Ma, Satoshi Nakamura, Matteo Negri, Jan Niehues, Juan Pino, Elizabeth Salesky, Sebastian Stüker, Katsuhito Sudoh, Marco Turchi, Alexander Waibel, Changhan Wang, Matthew Wiesner
The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2021) featured this year four shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Multilingual speech translation, (iv) Low-resource speech translation.
no code implementations • ACL (IWSLT) 2021 • Tuan Nam Nguyen, Thai Son Nguyen, Christian Huber, Ngoc-Quan Pham, Thanh-Le Ha, Felix Schneider, Sebastian Stüker
We describe a system in both cascaded condition and end-to-end condition.
no code implementations • ACL (IWSLT) 2021 • Ngoc-Quan Pham, Tuan Nam Nguyen, Thanh-Le Ha, Sebastian Stüker, Alexander Waibel, Dan He
This paper contains the description for the submission of Karlsruhe Institute of Technology (KIT) for the multilingual TEDx translation task in the IWSLT 2021 evaluation campaign.
no code implementations • IWSLT 2017 • Mauro Cettolo, Marcello Federico, Luisa Bentivogli, Jan Niehues, Sebastian Stüker, Katsuhito Sudoh, Koichiro Yoshino, Christian Federmann
The IWSLT 2017 evaluation campaign has organised three tasks.
no code implementations • IWSLT 2017 • Thai-Son Nguyen, Markus Müller, Matthias Sperber, Thomas Zenkel, Sebastian Stüker, Alex Waibel
For the English lecture task, our best combination system has a WER of 8. 3% on the tst2015 development set while our other combinations gained 25. 7% WER for German lecture tasks.
no code implementations • AACL (lifelongnlp) 2020 • Christian Huber, Juan Hussain, Tuan-Nam Nguyen, Kaihang Song, Sebastian Stüker, Alexander Waibel
This problem is even bigger for end-to-end speech recognition systems that only accept transcribed speech as training data, which is harder and more expensive to obtain than text data.
no code implementations • COLING (WANLP) 2020 • Juan Hussain, Mohammed Mediani, Moritz Behr, M. Amin Cheragui, Sebastian Stüker, Alexander Waibel
As this is a very specific domain, in addition to the linguistic challenges posed by translating between Arabic and German, we also focus in this paper on the methods we implemented for adapting our speech translation system to the domain of this psychiatric interview.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +7
no code implementations • 17 Oct 2023 • Jie Pu, Thai-Son Nguyen, Sebastian Stüker
In this paper, we investigate the usage of large language models (LLMs) to improve the performance of competitive speech recognition systems.
1 code implementation • 5 Jul 2021 • Christian Huber, Juan Hussain, Sebastian Stüker, Alexander Waibel
To alleviate this problem we supplement an end-to-end ASR system with a word/phrase memory and a mechanism to access this memory to recognize the words and phrases correctly.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 9 Mar 2020 • Thai-Son Nguyen, Sebastian Stüker, Alex Waibel
We show that for the hybrid models, supplying additional training data from other domains with mismatched acoustic conditions does not increase the performance on specific domains.
no code implementations • 30 Apr 2019 • Ngoc-Quan Pham, Thai-Son Nguyen, Jan Niehues, Markus Müller, Sebastian Stüker, Alexander Waibel
Recently, end-to-end sequence-to-sequence models for speech recognition have gained significant interest in the research community.
no code implementations • 5 Jul 2018 • Markus Müller, Sebastian Stüker, Alex Waibel
Multilingual Speech Recognition is one of the most costly AI problems, because each language (7, 000+) and even different accents require their own acoustic models to obtain best recognition performance.
1 code implementation • 26 Mar 2018 • Matthias Sperber, Jan Niehues, Graham Neubig, Sebastian Stüker, Alex Waibel
Self-attention is a method of encoding sequences of vectors by relating these vectors to each-other based on pairwise similarities.
no code implementations • 13 Nov 2017 • Markus Müller, Sebastian Stüker, Alex Waibel
We evaluated the use of different language combinations as well as the addition of Language Feature Vectors (LFVs).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 13 Nov 2017 • Markus Müller, Sebastian Stüker, Alex Waibel
In this work, we focus on multilingual systems based on recurrent neural networks (RNNs), trained using the Connectionist Temporal Classification (CTC) loss function.
no code implementations • 15 Aug 2017 • Thomas Zenkel, Ramon Sanabria, Florian Metze, Jan Niehues, Matthias Sperber, Sebastian Stüker, Alex Waibel
The CTC loss function maps an input sequence of observable feature vectors to an output sequence of symbols.
1 code implementation • 2 Jun 2017 • Robin Ruede, Markus Müller, Sebastian Stüker, Alex Waibel
BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues.