Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, IEEE Signal Processing Magazine, vol.29, issue.6, pp.82-97, 2012. ,
DOI : 10.1109/MSP.2012.2205597
Data Augmentation for deep neural network acoustic modeling, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1469-1477, 2015. ,
DOI : 10.1109/ICASSP.2014.6854671
A time delay neural network architecture for efficient modeling of long temporal contexts, Proc. Interspeech, pp.2440-2444, 2015. ,
Deep beamforming and data augmentation for robust speech recognition: Results of the 4th CHiME challenge, Proc. CHiME, pp.18-20, 2016. ,
Data augmentation for low resource languages, Proc. Interspeech, pp.810-814, 2014. ,
Deep Speech 2: End-to-end speech recognition in English and Mandarin, Proc. ICML, 2016. ,
On the relationship between Early-to-Late Ratio of Room Impulse Responses and ASR performance in reverberant environments, Speech Communication, vol.76, pp.170-185, 2016. ,
DOI : 10.1016/j.specom.2015.09.004
A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research, EURASIP Journal on Advances in Signal Processing, vol.87, issue.7, p.7, 2016. ,
DOI : 10.1186/s13634-016-0306-6
The Automatic Speech recogition In Reverberant Environments (ASpIRE) challenge, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.547-554, 2015. ,
DOI : 10.1109/ASRU.2015.7404843
The third ???CHiME??? speech separation and recognition challenge: Analysis and outcomes, Computer Speech & Language ,
DOI : 10.1016/j.csl.2016.10.005
URL : https://hal.archives-ouvertes.fr/hal-01382108
An analysis of environment, microphone and data simulation mismatches in robust speech recognition, Computer Speech & Language ,
DOI : 10.1016/j.csl.2016.11.005
URL : https://hal.archives-ouvertes.fr/hal-01399180
Noise Perturbation Improves Supervised Speech Separation, Proc. LVA/ICA, pp.83-90, 2015. ,
DOI : 10.1007/978-3-319-22482-4_10
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification, IEEE Signal Processing Letters, vol.24, issue.3, 2016. ,
DOI : 10.1109/LSP.2017.2657381
URL : http://arxiv.org/abs/1608.04363
Deep convolutional neural networks and data augmentation for acoustic event detection, 2016. ,
DOI : 10.21437/interspeech.2016-805
A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010. ,
DOI : 10.1109/TKDE.2009.191
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.9185
Improving predictive inference under covariate shift by weighting the log-likelihood function, Journal of Statistical Planning and Inference, vol.90, issue.2, pp.227-244, 2000. ,
DOI : 10.1016/S0378-3758(00)00115-4
Learning and evaluating classifiers under sample selection bias, Twenty-first international conference on Machine learning , ICML '04, pp.114-121, 2004. ,
DOI : 10.1145/1015330.1015425
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.6557
Domain adaptation for statistical classifiers, Journal of Artificial Intelligence Research, vol.26, pp.101-126, 2006. ,
Unsupervised domain adaptation by backpropagation Data selection by sequence summarizing neural network in mismatch condition training, Proc. ICML Proc. Interspeech, pp.1180-1189, 2015. ,
Domain adaptation with coupled subspaces, Proc. AISTATS, pp.173-181, 2011. ,
Semiparametric density estimation under a two-sample density ratio model, Bernoulli, vol.10, issue.4, pp.583-604, 2004. ,
DOI : 10.3150/bj/1093265631
Mismatched Training and Test Distributions Can Outperform Matched Ones, Neural Computation, vol.7, issue.2, pp.365-387, 2015. ,
DOI : 10.1145/1015330.1015425
Unsupervised network adaptation and phonetically-oriented system combination for the CHiME-4 challenge, Proc. CHiME, pp.49-51, 2016. ,
The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.436-443, 2015. ,
DOI : 10.1109/ASRU.2015.7404828
Robust speech recognition using beamforming with adaptive microphone gains and multichannel noise reduction, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.460-467, 2015. ,
DOI : 10.1109/ASRU.2015.7404831
The USTCiFlytek system for CHiME-4 challenge, Proc. CHiME, pp.36-38, 2016. ,
BLSTM supported GEV beamformer front-end for the 3RD CHiME challenge, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.444-451, 2015. ,
DOI : 10.1109/ASRU.2015.7404829
Discriminative learning for differing training and test distributions, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.81-88, 2007. ,
DOI : 10.1145/1273496.1273507
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.75.5892
The third ???CHiME??? speech separation and recognition challenge: Dataset, task and baselines, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.504-511, 2015. ,
DOI : 10.1109/ASRU.2015.7404837
URL : https://hal.archives-ouvertes.fr/hal-01211376
Maximum likelihood linear transformations for HMM-based speech recognition, Computer Speech & Language, vol.12, issue.2, pp.75-98, 1998. ,
DOI : 10.1006/csla.1998.0043
Maximum likelihood modeling with Gaussian distributions for classification, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), pp.661-664, 1998. ,
DOI : 10.1109/ICASSP.1998.675351
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.2128