Independent Component Analysis and Time-Frequency Masking for Speech Recognition in Multitalker Conditions, EURASIP Journal on Audio, Speech, and Music Processing, vol.28, issue.4, pp.1-13, 2010. ,
DOI : 10.1109/89.326616
URL : http://doi.org/10.1186/1687-4722-2010-651420
Front-end, back-end, and hybrid techniques for noise-robust speech recognition, " in Robust Speech Recognition of Uncertain or Missing Data, pp.67-99, 2011. ,
Using observation uncertainty in HMM decoding, Proc. Interspeech, pp.1561-1564, 2002. ,
Speaker verification in noise using a stochastic version of the weighted Viterbi algorithm, IEEE Transactions on Speech and Audio Processing, vol.10, issue.3, pp.158-166, 2002. ,
DOI : 10.1109/TSA.2002.1001980
Dynamic compensation of HMM variances using the feature enhancement uncertainty computed from a parametric model of speech distortion, IEEE Transactions on Speech and Audio Processing, vol.13, issue.3, pp.412-421, 2005. ,
DOI : 10.1109/TSA.2005.845814
Joint uncertainty decoding for noise robust speech recognition, Proc. Interspeech, pp.3129-3132, 2005. ,
Model-based feature enhancement with uncertainty decoding for noise robust ASR, Speech Communication, vol.48, issue.11, pp.1502-1514, 2006. ,
DOI : 10.1016/j.specom.2005.12.006
URL : https://lirias.kuleuven.be/bitstream/123456789/72637/1/pw2514.pdf
Static and Dynamic Variance Compensation for Recognition of Reverberant Speech With Dereverberation Preprocessing, IEEE Transactions on Audio, Speech, and Language Processing, vol.17, issue.2, pp.324-334, 2009. ,
DOI : 10.1109/TASL.2008.2010214
Cluster-based dynamic variance adaptation for interconnecting speech enhancement pre-processor and speech recognizer, Computer Speech & Language, vol.27, issue.1, pp.350-368, 2013. ,
DOI : 10.1016/j.csl.2012.07.001
Joint uncertainty decoding for noise robust subspace Gaussian mixture models, IEEE Transactions on Audio, Speech, and Language Processing, vol.21, issue.9, pp.1791-1804, 2013. ,
Integration of short-time Fourier domain speech enhancement and observation uncertainty techniques for robust automatic speech recognition, 2010. ,
Computing MMSE Estimates and Residual Uncertainty Directly in the Feature Domain of ASR using STFT Domain Speech Distortion Models, IEEE Transactions on Audio, Speech, and Language Processing, vol.21, issue.5, pp.1023-1034, 2013. ,
DOI : 10.1109/TASL.2013.2244085
Uncertainty-based learning of acoustic models from noisy data, Computer Speech & Language, vol.27, issue.3, pp.874-894, 2013. ,
DOI : 10.1016/j.csl.2012.07.002
URL : https://hal.archives-ouvertes.fr/hal-00717992
GMM-based significance decoding, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.6827-6831, 2013. ,
DOI : 10.1109/ICASSP.2013.6638984
Fusion of multiple uncertainty estimators and propagators for noise robust ASR, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5512-5516, 2014. ,
DOI : 10.1109/ICASSP.2014.6854657
URL : https://hal.archives-ouvertes.fr/hal-00955185
Propagation of uncertainty through multilayer perceptrons for robust automatic speech recognition, Proc. Interspeech, pp.461-464, 2011. ,
Uncertainty propagation through deep neural networks, Proc. Interspeech, pp.3561-3565, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01162550
Uncertainty training and decoding methods of deep neural networks based on stochastic representation of enhanced features, Proc. Interspeech, pp.3541-3545, 2015. ,
Accounting for the residual uncertainty of multi-layer perceptron based features, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.6859-6863, 2014. ,
DOI : 10.1109/ICASSP.2014.6854929
Uncertainty decoding for DNN-HMM hybrid systems based on numerical sampling, Proc. Interspeech, pp.3556-3560, 2015. ,
DOI : 10.1109/icassp.2016.7472781
Performance analysis of the Aurora large vocabulary baseline system, Proc. EUSIPCO, pp.553-556, 2004. ,
The second ‘CHiME’ speech separation and recognition challenge: An overview of challenge systems and outcomes, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp.162-167, 2013. ,
DOI : 10.1109/ASRU.2013.6707723
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
New extension of the Kalman filter to nonlinear systems, Signal Processing, Sensor Fusion, and Target Recognition VI, pp.182-193, 1997. ,
DOI : 10.1117/12.280797
A General Flexible Framework for the Handling of Prior Information in Audio Source Separation, IEEE Transactions on Audio, Speech, and Language Processing, vol.20, issue.4, pp.1118-1133, 2012. ,
DOI : 10.1109/TASL.2011.2172425
URL : https://hal.archives-ouvertes.fr/inria-00536917
The flexible audio source separation toolbox version 2.0, ICASSP Show & Tell, 2014. ,
The Kaldi speech recognition toolkit, Proc. ASRU, pp.1-4, 2011. ,
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
Transforming Binary Uncertainties for Robust Speech Recognition, IEEE Transactions on Audio, Speech and Language Processing, vol.15, issue.7, pp.2130-2140, 2007. ,
DOI : 10.1109/TASL.2007.901836
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.133.6752