Search Results for author:
Found 17 papers, 3 papers with code
The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018
In total we improve by 6. 8{\%} BLEU over our last year{'}s submission and by 4. 8{\%} BLEU over the winning system of the 2017 German→English task.
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation
This work investigates an alternative model for neural machine translation (NMT) and proposes a novel architecture, where we employ a multi-dimensional long short-term memory (MDLSTM) for translation modeling.
The RWTH Aachen University Machine Translation Systems for WMT 2019
For the De-En task, none of the tested methods gave a significant improvement over last years winning system and we end up with the same performance, resulting in 39. 6{\%} BLEU on newstest2019.
On using 2D sequence-to-sequence models for speech recognition
Attention-based sequence-to-sequence models have shown promising results in automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
On Using SpecAugment for End-to-End Speech Translation
This work investigates a simple data augmentation technique, SpecAugment, for end-to-end speech translation.
A Comparative Study on End-to-end Speech to Text Translation
Recent advances in deep learning show that end-to-end speech to text translation model is a promising approach to direct the speech translation field.
Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture
Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs.
Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University
AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Tight Integrated End-to-End Training for Cascaded Speech Translation
Direct speech translation is an alternative method to avoid error propagation; however, its performance is often behind the cascade system.
Two-Way Neural Machine Translation: A Proof of Concept for Bidirectional Translation Modeling using a Two-Dimensional Grid
Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality target sentence.
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 2021
This paper describes the offline and simultaneous speech translation systems developed at AppTek for IWSLT 2021.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Neural Speech Translation at AppTek
This work describes AppTek’s speech translation pipeline that includes strong state-of-the-art automatic speech recognition (ASR) and neural machine translation (NMT) components.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
The RWTH Aachen Machine Translation Systems for IWSLT 2017
This work describes the Neural Machine Translation (NMT) system of the RWTH Aachen University developed for the English$German tracks of the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2017.
Automatic Video Dubbing at AppTek
The revoicing usually comes with a changed script, mostly in a different language, and the revoicing should reproduce the original emotions, coherent with the body language, and lip synchronized.
Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text
Automatic Arabic diacritization is useful in many applications, ranging from reading support for language learners to accurate pronunciation predictor for downstream tasks like speech synthesis.
