Követés
Ralf Schlüter
Ralf Schlüter
Machine Learning and Human Language Technology, Lehrstuhl Informatik 6, RWTH Aachen University
E-mail megerősítve itt: cs.rwth-aachen.de - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
Lstm neural networks for language modeling.
M Sundermeyer, R Schlüter, H Ney
Interspeech 2012, 194-197, 2012
27762012
Confidence measures for large vocabulary continuous speech recognition
F Wessel, R Schlüter, K Macherey, H Ney
IEEE Transactions on speech and audio processing 9 (3), 288-298, 2001
6542001
From feedforward to recurrent LSTM neural networks for language modeling
M Sundermeyer, H Ney, R Schlüter
IEEE/ACM Transactions on Audio, Speech, and Language Processing 23 (3), 517-529, 2015
6172015
Computing mel-frequency cepstral coefficients on the power spectrum
S Molau, M Pitz, R Schlüter, H Ney
Acoustics, Speech, and Signal Processing, 2001. Proceedings.(ICASSP'01 …, 2001
4042001
Vocal Tract Normalization Equals Linear Transformation in Cepstral Space.
M Pitz, S Molau, R Schlüter, H Ney
European Conference on Speech Communication and Technology (EUROSPEECH …, 2001
349*2001
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
3062018
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
2972019
A comparison of transformer and lstm encoder decoder models for asr
A Zeyer, P Bahar, K Irie, R Schlüter, H Ney
2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 8-15, 2019
2502019
Acoustic Modeling with Deep Neural Networks using Raw Time Signal for LVCSR.
Z Tüske, P Golik, R Schlüter, H Ney
Interspeech, 890-894, 2014
2462014
A comprehensive study of deep bidirectional LSTM RNNs for acoustic modeling in speech recognition
A Zeyer, P Doetsch, P Voigtlaender, R Schlüter, H Ney
2017 IEEE international conference on acoustics, speech and signal …, 2017
2242017
Gammatone Features and Feature Combination for Large Vocabulary Speech Recognition
R Schlüter, I Bezrukov, H Wagner, H Ney
2007 IEEE International Conference on Acoustics, Speech and Signal …, 2007
2202007
Language modeling with deep transformers
K Irie, A Zeyer, R Schlüter, H Ney
arXiv preprint arXiv:1905.04226, 2019
2112019
Comparison of Feedforward and Recurrent Neural Network Language Models
M Sundermeyer, I Oparin, JL Gauvain, B Freiberg, R Schlüter, H Ney
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International …, 2013
1722013
Using Word Probabilities as Confidence Measures
F Wessel, K Macherey, R Schlüter
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE …, 1998
1571998
The RWTH Aachen University Open Source Speech Recognition System.
D Rybach, C Gollan, G Heigold, B Hoffmeister, J Lööf, R Schlüter, H Ney
Interspeech, 2111-2114, 2009
1472009
Convolutional neural networks for acoustic modeling of raw time signal in LVCSR
P Golik, Z Tüske, R Schlüter, H Ney
Sixteenth annual conference of the international speech communication …, 2015
1442015
Comparison of Discriminative Training Criteria and Optimization Methods for Speech Recognition
R Schlüter, W Macherey, B Müller, H Ney
Speech Communication 34 (3), 287-310, 2001
1322001
LSTM, GRU, highway and a bit of attention: An empirical overview for language modeling in speech recognition
K Irie, Z Tüske, T Alkhouli, R Schlüter, H Ney
Interspeech, 3519-3523, 2016
1232016
Multilingual representations for low resource speech recognition and keyword search
J Cui, B Kingsbury, B Ramabhadran, A Sethy, K Audhkhasi, X Cui, ...
2015 IEEE workshop on automatic speech recognition and understanding (ASRU …, 2015
1142015
RASR-The RWTH Aachen University Open Source Speech Recognition Toolkit
D Rybach, S Hahn, P Lehnen, D Nolden, M Sundermeyer, Z Tüske, ...
Proc. IEEE Automatic Speech Recognition and Understanding Workshop, 2011
1132011
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