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Kazuki Irie
Kazuki Irie
The Swiss AI Lab - IDSIA, University of Lugano
Verified email at idsia.ch - Homepage
Title
Cited by
Cited by
Year
Improved training of end-to-end attention models for speech recognition
A Zeyer, K Irie, R Schlüter, H Ney
Interspeech 2018, 2018
2532018
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
Interspeech 2019, 2019
2292019
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
1472019
Language modeling with deep transformers
K Irie, A Zeyer, R Schlüter, H Ney
Interspeech 2019, 2019
1212019
A Comparison of Transformer and LSTM Encoder Decoder Models for ASR
A Zeyer, P Bahar, K Irie, R Schlüter, H Ney
ASRU 2019, IEEE Automatic Speech Recognition and Understanding Workshop …, 2019
1132019
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
802016
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
62*2019
Linear transformers are secretly fast weight programmers
I Schlag*, K Irie*, J Schmidhuber
International Conference on Machine Learning, 9355-9366, 2021
49*2021
The RWTH/UPB/FORTH system combination for the 4th CHiME challenge evaluation
T Menne
Deutsche Nationalbibliothek, 2016
452016
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 2020, 2020
352020
The devil is in the detail: Simple tricks improve systematic generalization of transformers
R Csordás, K Irie, J Schmidhuber
EMNLP 2021, 2021
342021
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
322019
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
282018
Going beyond linear transformers with recurrent fast weight programmers
K Irie*, I Schlag*, R Csordás, J Schmidhuber
Advances in Neural Information Processing Systems 34, 7703-7717, 2021
232021
On efficient training of word classes and their application to recurrent neural network language models
R Botros, K Irie, M Sundermeyer, H Ney
Interspeech 2016, 2015
192015
Investigation on log-linear interpolation of multi-domain neural network language model
Z Tüske, K Irie, R Schlüter, H Ney
ICASSP 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016
182016
Prediction of LSTM-RNN Full Context States as a Subtask for N-gram Feedforward Language Models
K Irie, Z Lei, R Schlüter, H Ney
ICASSP 2018, IEEE International Conference on Acoustics, Speech and Signal …, 2018
162018
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
152015
How Much Self-Attention Do We Need? Trading Attention for Feed-Forward Layers
K Irie, A Gerstenberger, R Schlüter, H Ney
ICASSP 2020, 6154-6158, 2020
92020
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 2018, 392-395, 2018
82018
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