| Improved training of end-to-end attention models for speech recognition A Zeyer, K Irie, R Schlüter, H Ney arXiv preprint arXiv:1805.03294, 2018 | 240 | 2018 |
| RWTH ASR Systems for LibriSpeech: Hybrid vs Attention--w/o Data Augmentation C Lüscher, E Beck, K Irie, M Kitza, W Michel, A Zeyer, R Schlüter, H Ney arXiv preprint arXiv:1905.03072, 2019 | 208 | 2019 |
| Lingvo: a modular and scalable framework for sequence-to-sequence modeling J Shen, P Nguyen, Y Wu, Z Chen, MX Chen, Y Jia, A Kannan, T Sainath, ... arXiv preprint arXiv:1902.08295, 2019 | 135 | 2019 |
| Language modeling with deep transformers K Irie, A Zeyer, R Schlüter, H Ney arXiv preprint arXiv:1905.04226, 2019 | 107 | 2019 |
| A Comparison of Transformer and LSTM Encoder Decoder Models for ASR A Zeyer, P Bahar, K Irie, R Schlüter, H Ney IEEE Automatic Speech Recognition and Understanding Workshop, Sentosa, Singapore, 2019 | 94 | 2019 |
| LSTM, GRU, highway and a bit of attention: an empirical overview for language modeling in speech recognition K Irie, Z Tuske, T Alkhouli, R Schluter, H Ney Interspeech, 2016, 3519-3523, 2016 | 79 | 2016 |
| On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition K Irie, R Prabhavalkar, A Kannan, A Bruguier, D Rybach, P Nguyen Proc. Interspeech 2019, 3800-3804, 2019 | 58* | 2019 |
| The RWTH/UPB/FORTH system combination for the 4th CHiME challenge evaluation T Menne Deutsche Nationalbibliothek, 2016 | 41 | 2016 |
| The RWTH ASR System for TED-LIUM Release 2: Improving Hybrid HMM with SpecAugment W Zhou, W Michel, K Irie, M Kitza, R Schlüter, H Ney ICASSP, Barcelona, Spain, 2020 | 30 | 2020 |
| Linear transformers are secretly fast weight programmers I Schlag, K Irie, J Schmidhuber International Conference on Machine Learning, 9355-9366, 2021 | 29 | 2021 |
| Training language models for long-span cross-sentence evaluation K Irie, A Zeyer, R Schlüter, H Ney IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2019 | 29 | 2019 |
| The devil is in the detail: Simple tricks improve systematic generalization of transformers R Csordás, K Irie, J Schmidhuber arXiv preprint arXiv:2108.12284, 2021 | 27 | 2021 |
| RADMM: Recurrent Adaptive Mixture Model with Applications to Domain Robust Language Modeling K Irie, S Kumar, M Nirschl, H Liao IEEE International Conference on Acoustics, Speech, and Signal Processing …, 2018 | 26 | 2018 |
| On efficient training of word classes and their application to recurrent neural network language models R Botros, K Irie, M Sundermeyer, H Ney Sixteenth Annual Conference of the International Speech Communication …, 2015 | 18 | 2015 |
| Going beyond linear transformers with recurrent fast weight programmers K Irie, I Schlag, R Csordás, J Schmidhuber Advances in Neural Information Processing Systems 34, 2021 | 17 | 2021 |
| Investigation on log-linear interpolation of multi-domain neural network language model Z Tüske, K Irie, R Schlüter, H Ney 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 17 | 2016 |
| Bag-of-words input for long history representation in neural network-based language models for speech recognition K Irie, R Schlüter, H Ney Interspeech, 2015, 2015 | 15 | 2015 |
| Prediction of LSTM-RNN Full Context States as a Subtask for N-gram Feedforward Language Models K Irie, Z Lei, R Schlüter, H Ney IEEE International Conference on Acoustics, Speech and Signal Processing …, 2018 | 14 | 2018 |
| How Much Self-Attention Do We Need? Trading Attention for Feed-Forward Layers K Irie, A Gerstenberger, R Schlüter, H Ney ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 9 | 2020 |
| Investigation on estimation of sentence probability by combining forward, backward and bi-directional LSTM-RNNs K Irie, Z Lei, L Deng, R Schlüter, H Ney INTERSPEECH, 392-395, 2018 | 8 | 2018 |
