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2020 – today
- 2020
[b1]Patrick Doetsch:
Alignment models for recurrent neural networks. RWTH Aachen University, Germany, 2020
2010 – 2019
- 2018
- [c23]Eugen Beck, Albert Zeyer, Patrick Doetsch, André Merboldt, Ralf Schlüter, Hermann Ney:
Sequence Modeling and Alignment for LVCSR-Systems. ITG Symposium on Speech Communication 2018: 1-5 - [c22]Javier Jorge, Adria A. Martinez-Villaronga, Pavel Golik
, Adrià Giménez, Joan Albert Silvestre-Cerdà, Patrick Doetsch, Vicent Andreu Císcar, Hermann Ney, Alfons Juan, Albert Sanchís:
MLLP-UPV and RWTH Aachen Spanish ASR Systems for the IberSpeech-RTVE 2018 Speech-to-Text Transcription Challenge. IberSPEECH 2018: 257-261 - [c21]Eugen Beck, Mirko Hannemann, Patrick Dötsch, Ralf Schlüter, Hermann Ney:
Segmental Encoder-Decoder Models for Large Vocabulary Automatic Speech Recognition. INTERSPEECH 2018: 766-770 - 2017
- [j1]Patrick Doetsch, Mirko Hannemann, Ralf Schlüter, Hermann Ney:
Inverted Alignments for End-to-End Automatic Speech Recognition. IEEE J. Sel. Top. Signal Process. 11(8): 1265-1273 (2017) - [c20]Albert Zeyer, Patrick Doetsch, Paul Voigtlaender, Ralf Schlüter, Hermann Ney:
A comprehensive study of deep bidirectional LSTM RNNS for acoustic modeling in speech recognition. ICASSP 2017: 2462-2466 - [c19]Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilia Kulikov, Ralf Schlüter, Hermann Ney:
Returnn: The RWTH extensible training framework for universal recurrent neural networks. ICASSP 2017: 5345-5349 - [i3]Patrick Doetsch, Pavel Golik, Hermann Ney:
A comprehensive study of batch construction strategies for recurrent neural networks in MXNet. CoRR abs/1705.02414 (2017) - 2016
- [c18]Dewi Suryani, Patrick Doetsch, Hermann Ney:
On the Benefits of Convolutional Neural Network Combinations in Offline Handwriting Recognition. ICFHR 2016: 193-198 - [c17]Paul Voigtlaender, Patrick Doetsch, Hermann Ney:
Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks. ICFHR 2016: 228-233 - [c16]Patrick Doetsch, Albert Zeyer, Hermann Ney:
Bidirectional Decoder Networks for Attention-Based End-to-End Offline Handwriting Recognition. ICFHR 2016: 361-366 - [c15]Ralf Schlüter, Patrick Doetsch, Pavel Golik, Markus Kitza, Tobias Menne, Kazuki Irie, Zoltán Tüske, Albert Zeyer:
Automatic Speech Recognition Based on Neural Networks. SPECOM 2016: 3-17 - [i2]Albert Zeyer, Patrick Doetsch, Paul Voigtlaender, Ralf Schlüter, Hermann Ney:
A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition. CoRR abs/1606.06871 (2016) - [i1]Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf Schlüter, Hermann Ney:
RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks. CoRR abs/1608.00895 (2016) - 2015
- [c14]Paul Voigtlaender, Patrick Doetsch, Simon Wiesler, Ralf Schlüter, Hermann Ney:
Sequence-discriminative training of recurrent neural networks. ICASSP 2015: 2100-2104 - [c13]Mahdi Hamdani, Patrick Doetsch, Hermann Ney:
Bagging by design for continuous Handwriting Recognition using multi-objective particle swarm optimization. ICDAR 2015: 256-260 - [c12]Mahdi Hamdani, M. Ali Basha Shaik, Patrick Doetsch, Hermann Ney:
Investigation of Segmental Conditional Random Fields for large vocabulary handwriting recognition. ICDAR 2015: 261-265 - 2014
- [c11]Mahdi Hamdani, Patrick Doetsch, Michal Kozielski, Amr El-Desoky Mousa, Hermann Ney:
The RWTH Large Vocabulary Arabic Handwriting Recognition System. Document Analysis Systems 2014: 111-115 - [c10]Michal Kozielski, Patrick Doetsch, Mahdi Hamdani, Hermann Ney:
Multilingual Off-Line Handwriting Recognition in Real-World Images. Document Analysis Systems 2014: 121-125 - [c9]Patrick Doetsch, Michal Kozielski, Hermann Ney:
Fast and Robust Training of Recurrent Neural Networks for Offline Handwriting Recognition. ICFHR 2014: 279-284 - [c8]Michal Kozielski, Martin Matysiak, Patrick Doetsch, Ralf Schlüter, Hermann Ney:
Open-Lexicon Language Modeling Combining Word and Character Levels. ICFHR 2014: 343-348 - [c7]Mahdi Hamdani, Patrick Doetsch, Hermann Ney:
Improvement of Context Dependent Modeling for Arabic Handwriting Recognition. ICFHR 2014: 494-499 - [c6]Michal Kozielski, Malte Nuhn, Patrick Doetsch, Hermann Ney:
Towards Unsupervised Learning for Handwriting Recognition. ICFHR 2014: 549-554 - 2013
- [c5]Michal Kozielski, Patrick Doetsch, Hermann Ney:
Improvements in RWTH's System for Off-Line Handwriting Recognition. ICDAR 2013: 935-939 - [c4]Pavel Golik, Patrick Doetsch, Hermann Ney:
Cross-entropy vs. squared error training: a theoretical and experimental comparison. INTERSPEECH 2013: 1756-1760 - 2012
- [c3]Patrick Doetsch, Mahdi Hamdani, Hermann Ney, Adrià Giménez, Jesús Andrés-Ferrer, Alfons Juan:
Comparison of Bernoulli and Gaussian HMMs Using a Vertical Repositioning Technique for Off-Line Handwriting Recognition. ICFHR 2012: 3-7 - 2011
- [c2]Philippe Dreuw, Patrick Doetsch, Christian Plahl, Hermann Ney:
Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition. ICIP 2011: 3541-3544
2000 – 2009
- 2009
- [c1]Patrick Doetsch, Christian Buck, Pavlo Golik, Niklas Hoppe, Michael Kramp, Johannes Laudenberg, Christian Oberdörfer, Pascal Steingrube, Jens Forster, Arne Mauser:
Logistic Model Trees with AUC Split Criterion for the KDD Cup 2009 Small Challenge. KDD Cup 2009: 77-88
Coauthor Index
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