| The REVERB challenge: A common evaluation framework for dereverberation and recognition of reverberant speech K Kinoshita, M Delcroix, T Yoshioka, T Nakatani, E Habets, ... 2013 IEEE Workshop on Applications of Signal Processing to Audio and …, 2013 | 504 | 2013 |
| A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research K Kinoshita, M Delcroix, S Gannot, EA P. Habets, R Haeb-Umbach, ... EURASIP Journal on Advances in Signal Processing 2016 (1), 7, 2016 | 462 | 2016 |
| Anchored speech detection and speech recognition SHK Parthasarathi, B Hoffmeister, B King, R Maas US Patent 10,373,612, 2019 | 385 | 2019 |
| Making machines understand us in reverberant rooms: Robustness against reverberation for automatic speech recognition T Yoshioka, A Sehr, M Delcroix, K Kinoshita, R Maas, T Nakatani, ... IEEE Signal Processing Magazine 29 (6), 114-126, 2012 | 355 | 2012 |
| Reverberation model-based decoding in the logmelspec domain for robust distant-talking speech recognition A Sehr, R Maas, W Kellermann IEEE transactions on audio, speech, and language processing 18 (7), 1676-1691, 2010 | 80 | 2010 |
| Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning L Mošner, M Wu, A Raju, SHK Parthasarathi, K Kumatani, S Sundaram, ... ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 77 | 2019 |
| Synthasr: Unlocking synthetic data for speech recognition A Fazel, W Yang, Y Liu, R Barra-Chicote, Y Meng, R Maas, J Droppo arXiv preprint arXiv:2106.07803, 2021 | 75 | 2021 |
| DiPCo--Dinner Party Corpus M Van Segbroeck, A Zaid, K Kutsenko, C Huerta, T Nguyen, X Luo, ... arXiv preprint arXiv:1909.13447, 2019 | 73 | 2019 |
| Wav2vec-c: A self-supervised model for speech representation learning S Sadhu, D He, CW Huang, SH Mallidi, M Wu, A Rastrow, A Stolcke, ... arXiv preprint arXiv:2103.08393, 2021 | 72 | 2021 |
| Detecting system-directed speech RMR Maas, SHR Mallidi, S Matsoukas, B Hoffmeister US Patent 11,361,763, 2022 | 71 | 2022 |
| Efficient minimum word error rate training of rnn-transducer for end-to-end speech recognition J Guo, G Tiwari, J Droppo, M Van Segbroeck, CW Huang, A Stolcke, ... arXiv preprint arXiv:2007.13802, 2020 | 68 | 2020 |
| Device-directed utterance detection SH Mallidi, R Maas, K Goehner, A Rastrow, S Matsoukas, B Hoffmeister arXiv preprint arXiv:1808.02504, 2018 | 64 | 2018 |
| Multiresolution and multimodal speech recognition with transformers G Paraskevopoulos, S Parthasarathy, A Khare, S Sundaram arXiv preprint arXiv:2004.14840, 2020 | 61 | 2020 |
| Improving ASR confidence scores for Alexa using acoustic and hypothesis embeddings P Swarup, R Maas, S Garimella, SH Mallidi, B Hoffmeister | 52 | 2019 |
| Language model adaptation A Gandhe, A Rastrow, RMR Maas, B Hoffmeister US Patent 11,302,310, 2022 | 48 | 2022 |
| Spatial diffuseness features for DNN-based speech recognition in noisy and reverberant environments A Schwarz, C Huemmer, R Maas, W Kellermann 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 46 | 2015 |
| A stereophonic acoustic signal extraction scheme for noisy and reverberant environments K Reindl, Y Zheng, A Schwarz, S Meier, R Maas, A Sehr, W Kellermann Computer Speech & Language 27 (3), 726-745, 2013 | 45 | 2013 |
| Robust speech recognition via anchor word representations B King, IF Chen, Y Vaizman, Y Liu, R Maas, SHK Parthasarathi, ... | 42 | 2017 |
| Towards a better understanding of the effect of reverberation on speech recognition performance A Sehr, EAP Habets, R Maas, W Kellermann Proc. IWAENC, 1-4, 2010 | 42 | 2010 |
| Combining acoustic embeddings and decoding features for end-of-utterance detection in real-time far-field speech recognition systems R Maas, A Rastrow, C Ma, G Lan, K Goehner, G Tiwari, S Joseph, ... 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 36 | 2018 |
Armin SehrOTH RegensburgVerified email at oth-regensburg.de
