| The Kaldi speech recognition toolkit D Povey, A Ghoshal, G Boulianne, L Burget, O Glembek, N Goel, ... IEEE 2011 workshop on automatic speech recognition and understanding, 2011 | 6625 | 2011 |
| Generating exact lattices in the WFST framework D Povey, M Hannemann, G Boulianne, L Burget, A Ghoshal, M Janda, ... 2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012 | 171 | 2012 |
| Generating exact lattices in the WFST framework D Povey, M Hannemann, G Boulianne, L Burget, A Ghoshal, M Janda, ... 2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012 | 171 | 2012 |
| Semi-supervised training of deep neural networks K Veselý, M Hannemann, L Burget 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 267-272, 2013 | 160 | 2013 |
| Score normalization and system combination for improved keyword spotting D Karakos, R Schwartz, S Tsakalidis, L Zhang, S Ranjan, T Ng, R Hsiao, ... 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 210-215, 2013 | 103 | 2013 |
| Combination of strongly and weakly constrained recognizers for reliable detection of OOVs L Burget, P Schwarz, P Matejka, M Hannemann, A Rastrow, C White, ... 2008 IEEE International Conference on Acoustics, Speech and Signal …, 2008 | 80 | 2008 |
| BUT BABEL system for spontaneous Cantonese. M Karafiát, F Grézl, M Hannemann, K Veselý, J Cernocký Interspeech 13, 2589-2593, 2013 | 44 | 2013 |
| BUT 2014 Babel system: analysis of adaptation in NN based systems. M Karafiát, F Grezl, K Veselý, M Hannemann, I Szöke, J Cernocký Interspeech, 3002-3006, 2014 | 36 | 2014 |
| Detection of out-of-vocabulary words in posterior based ASR H Ketabdar, M Hannemann, H Hermansky Eighth Annual Conference of the International Speech Communication Association, 2007 | 31 | 2007 |
| But neural network features for spontaneous Vietnamese in BABEL M Karafiát, F Grézl, M Hannemann, JH Černocký 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 24 | 2014 |
| Segmental Encoder-Decoder Models for Large Vocabulary Automatic Speech Recognition. E Beck, M Hannemann, P Doetsch, R Schlüter, H Ney Interspeech, 766-770, 2018 | 17 | 2018 |
| Recovery of rare words in lecture speech S Kombrink, M Hannemann, L Burget, H Heřmanský Text, Speech and Dialogue: 13th International Conference, TSD 2010, Brno …, 2010 | 14 | 2010 |
| Inverted Alignments for End-to-End Automatic Speech Recognition PDMHRSH Ney IEEE Journal of Selected Topics in Signal Processing, 2017 | 12 | 2017 |
| Similarity scoring for recognizing repeated out-of-vocabulary words M Hannemann, S Kombrink, M Karafiát, L Burget Eleventh Annual Conference of the International Speech Communication Association, 2010 | 12 | 2010 |
| Bayesian joint-sequence models for grapheme-to-phoneme conversion M Hannemann, J Trmal, L Ondel, S Kesiraju, L Burget 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 10 | 2017 |
| But ASR system for BABEL surprise evaluation 2014 M Karafiát, K Veselý, I Szoke, L Burget, F Grézl, M Hannemann, ... 2014 IEEE Spoken Language Technology Workshop (SLT), 501-506, 2014 | 10 | 2014 |
| Out-of-vocabulary word detection and beyond S Kombrink, M Hannemann, L Burget Detection and Identification of Rare Audiovisual Cues, 57-65, 2012 | 10 | 2012 |
| Connecting and Comparing Language Model Interpolation Techniques E Pusateri, C Van Gysel, R Botros, S Badaskar, M Hannemann, Y Oualil, ... arXiv preprint arXiv:1908.09738, 2019 | 9 | 2019 |
| Hierarchical HMM-based semantic concept labeling model KT Mengistu, M Hannemann, T Baum, A Wendemuth 2008 IEEE Spoken Language Technology Workshop, 57-60, 2008 | 7 | 2008 |
| Combining forward and backward search in decoding M Hannemann, D Povey, G Zweig 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 6 | 2013 |
