| The TUM Gait from Audio, Image and Depth (GAID) Database: Multimodal Recognition of Subjects and Traits M Hofmann, J Geiger, S Bachmann, B Schuller, G Rigoll Journal of Visual Communication and Image Representation (JVCI), Special …, 2014 | 141 | 2014 |
| Deep learning for environmentally robust speech recognition: An overview of recent developments Z Zhang, J Geiger, J Pohjalainen, AED Mousa, W Jin, B Schuller ACM Transactions on Intelligent Systems and Technology (TIST) 9 (5), 1-28, 2018 | 122 | 2018 |
| LARGE-SCALE AUDIO FEATURE EXTRACTION AND SVM FOR ACOUSTIC SCENE CLASSIFICATION JT Geiger, B Schuller, G Rigoll WASPAA, 2013 | 117 | 2013 |
| Robust speech recognition using long short-term memory recurrent neural networks for hybrid acoustic modelling JT Geiger, Z Zhang, F Weninger, B Schuller, G Rigoll Fifteenth annual conference of the international speech communication …, 2014 | 72 | 2014 |
| Feature enhancement by deep LSTM networks for ASR in reverberant multisource environments F Weninger, J Geiger, M Wöllmer, B Schuller, G Rigoll Computer Speech & Language 28 (4), 888-902, 2014 | 56 | 2014 |
| The Munich 2011 CHiME Challenge Contribution: NMF-BLSTM Speech Enhancement and Recognition for Reverberated Multisource Environments F Weninger, J Geiger, M Wöllmer, B Schuller, G Rigoll | 52 | 2011 |
| NON-NEGATIVE MATRIX FACTORIZATION FOR HIGHLY NOISE-ROBUST ASR: TO ENHANCE OR TO RECOGNIZE? F Weninger, M Wöllmer, J Geiger, B Schuller, JF Gemmeke, ... ICASSP, 2012 | 47 | 2012 |
| THE MERL/MELCO/TUM SYSTEM FOR THE REVERB CHALLENGE USING DEEP RECURRENT NEURAL NETWORK FEATURE ENHANCEMENT F Weninger, S Watanabe, J Le Roux, JR Hershey, Y Tachioka, J Geiger, ... REVERB Workshop, 2014 | 40 | 2014 |
| Acoustic gait-based person identification using hidden Markov models JT Geiger, M Kneißl, BW Schuller, G Rigoll Proceedings of the 2014 Workshop on Mapping Personality Traits Challenge and …, 2014 | 39 | 2014 |
| IMPROVING EVENT DETECTION FOR AUDIO SURVEILLANCE USING GABOR FILTERBANK FEATURES JT Geiger, K Helwani EUSIPCO, 2015 | 35 | 2015 |
| Memory-Enhanced Neural Networks and NMF for Robust ASR J Geiger, F Weninger, J Gemmeke, M Wollmer, B Schuller, G Rigoll Audio, Speech, and Language Processing, IEEE/ACM Transactions on 20 (6 …, 2014 | 32 | 2014 |
| THE TUM+ TUT+ KUL APPROACH TO THE 2ND CHIME CHALLENGE: MULTI-STREAM ASR EXPLOITING BLSTM NETWORKS AND SPARSE NMF JT Geiger, F Weninger, A Hurmalainen, JF Gemmeke, M Wöllmer, ... CHiME Workshop, 2013 | 32* | 2013 |
| GMM-UBM based open-set online speaker diarization J Geiger, F Wallhoff, G Rigoll Eleventh Annual Conference of the International Speech Communication Association, 2010 | 32 | 2010 |
| Speech Overlap Detection and Attribution Using Convolutive Non-Negative Sparse Coding R Vipperla, J Geiger, S Bozonnet, D Wang, N Evans, B Schuller, G Rigoll Proc. ICASSP, 4181-4184, 2012 | 28 | 2012 |
| RECOGNISING ACOUSTIC SCENES WITH LARGE-SCALE AUDIO FEATURE EXTRACTION AND SVM JT Geiger, B Schuller, G Rigoll | 26 | 2013 |
| Detecting Overlapping Speech with Long Short-Term Memory Recurrent Neural Networks JT Geiger, F Eyben, B Schuller, G Rigoll Interspeech, 2013 | 25 | 2013 |
| Investigating NMF speech enhancement for neural network based acoustic models JT Geiger, JF Gemmeke, B Schuller, G Rigoll Fifteenth Annual Conference of the International Speech Communication …, 2014 | 20 | 2014 |
| Noise robust ASR in reverberated multisource environments applying convolutive NMF and Long Short-Term Memory M Wöllmer, F Weninger, J Geiger, B Schuller, G Rigoll Computer Speech & Language 27 (3), 780-797, 2013 | 18 | 2013 |
| THE MUNICH FEATURE ENHANCEMENT APPROACH TO THE 2ND CHIME CHALLENGE USING BLSTM RECURRENT NEURAL NETWORKS F Weninger, J Geiger, M Wöllmer, B Schuller, G Rigoll | 18 | 2013 |
| Convolutive Non-Negative Sparse Coding and New Features for Speech Overlap Handling in Speaker Diarization JT Geiger, R Vipperla, S Bozonnet, N Evans, B Schuller, G Rigoll Interspeech, 2012 | 18 | 2012 |
Gerhard RigollProfessor for Human-Machine Communication, TU MünchenVerified email at gerhard-rigoll.de
