Roland Maas
Roland Maas
Sr. Science Manager at Amazon
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Zitiert von
Zitiert von
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
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, 1-19, 2016
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
Anchored speech detection and speech recognition
SHK Parthasarathi, B Hoffmeister, B King, R Maas
US Patent 10,373,612, 2019
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
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
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
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
Device-directed utterance detection
SH Mallidi, R Maas, K Goehner, A Rastrow, S Matsoukas, B Hoffmeister
arXiv preprint arXiv:1808.02504, 2018
Multiresolution and multimodal speech recognition with transformers
G Paraskevopoulos, S Parthasarathy, A Khare, S Sundaram
arXiv preprint arXiv:2004.14840, 2020
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
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
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
Improving ASR confidence scores for Alexa using acoustic and hypothesis embeddings
P Swarup, R Maas, S Garimella, SH Mallidi, B Hoffmeister
Robust speech recognition via anchor word representations
B King, IF Chen, Y Vaizman, Y Liu, R Maas, SHK Parthasarathi, ...
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
DiPCo--Dinner Party Corpus
M Van Segbroeck, A Zaid, K Kutsenko, C Huerta, T Nguyen, X Luo, ...
arXiv preprint arXiv:1909.13447, 2019
Detecting system-directed speech
RMR Maas, SHR Mallidi, S Matsoukas, B Hoffmeister
US Patent 11,361,763, 2022
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
On the application of reverberation suppression to robust speech recognition
R Maas, EAP Habets, A Sehr, W Kellermann
2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012
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