A study of temporal features and frequency characteristics in American English foreign accent, The Journal of the Acoustical Society of America, vol.102, issue.1, 1996. ,
DOI : 10.1121/1.419608
Fully Automated Non-native Speech Recognition using Confusion-based Acoustic Model Intergration, Proceedings of Eurospeech, pp.1369-1372, 2005. ,
DOI : 10.1109/icassp.2006.1660028
Deep neural network acoustic modeling for native and non-native Mandarin speech recognition, The 9th International Symposium on Chinese Spoken Language Processing, 2014. ,
DOI : 10.1109/ISCSLP.2014.6936617
Robust Adaptation to Non-native Accents in Automatic Speech Recognition, 2002. ,
DOI : 10.1007/3-540-36290-8
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, Deep Neural Networks for Acoustic Modeling in Speech recognition, pp.82-97, 2012. ,
DOI : 10.1109/MSP.2012.2205597
A Practical Guide to Training Restricted Boltzmann Machines. Utml tr 2010-003, 2010. ,
DOI : 10.1007/978-3-642-35289-8_32
URL : http://learning.cs.toronto.edu/%7Ehinton/absps/guideTR.pdf
Accent Modeling based on Pronunciation Dictionary Adaptation for Large Vocabulary Mandarin Speech Recognition, Proceedings of ICLSP, pp.818-821, 2000. ,
Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013. ,
DOI : 10.1109/ICASSP.2013.6639081
Multi-accent Deep Neural Network Acoustic Model with Accent-specific Top Layer using the KLD-regularized Model Adaptation, Proceedings of INTERSPEECH, 2014. ,
Using out-of-language data to improve an under-resourced speech recognizer, Speech Communication, vol.56, issue.0, pp.142-151, 2014. ,
DOI : 10.1016/j.specom.2013.01.007
Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.3761-3764, 2009. ,
DOI : 10.1109/ICASSP.2009.4960445
Cross-Lingual Subspace Gaussian Mixture Models for Low-Resource Speech Recognition, Speech and Language Processing, pp.17-27, 2014. ,
DOI : 10.1109/TASL.2013.2281575
URL : http://www.cstr.ed.ac.uk/downloads/publications/2013/lu_crosslingual13.pdf
Improving Low-resource CD-DNN-HMM using Dropout and Multilingual DNN Training, Proceedings of INTERSPEECH. pp, pp.2237-2241, 2013. ,
Dealing with Acoustic Mismatch for Training Multlingual Subspace Gaussian Mixture Models for Speech Recognition, Proceedings of ICASSP, pp.4893-4896, 2012. ,
DOI : 10.1109/icassp.2012.6289016
Making a Speech Recognizer Tolerate Non-native Speech through Gaussian Mixture Merging, Proceedings of ICALL'04, 2004. ,
Subspace Gaussian Mixture Models for speech recognition, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010. ,
DOI : 10.1109/ICASSP.2010.5495662
URL : http://research.microsoft.com/pubs/80931/ubmdoc.pdf
The subspace Gaussian mixture model???A structured model for speech recognition, Computer Speech & Language, vol.25, issue.2, pp.404-439, 2011. ,
DOI : 10.1016/j.csl.2010.06.003
The Kaldi Speech Recognition Toolkit, Proceedings of Workshop on Automatic Speech Recognition and Understanding, p.11, 2011. ,
TED-LIUM: An Automatic Speech Recognition Dedicated Corpus, Proceedings of LREC European Language Resources Association (ELRA), pp.125-129, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-01434928
Unsupervised cross-lingual knowledge transfer in DNN-based LVCSR, 2012 IEEE Spoken Language Technology Workshop (SLT), 2013. ,
DOI : 10.1109/SLT.2012.6424230
URL : http://www.cstr.ed.ac.uk/downloads/publications/2012/ps_slt2012.pdf
Acoustic Model Interpolation for Non-Native Speech Recognition, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, 2007. ,
DOI : 10.1109/ICASSP.2007.367243
Acoustic model merging using acoustic models from multilingual speakers for automatic speech recognition, 2014 International Conference on Asian Language Processing (IALP), 2014. ,
DOI : 10.1109/IALP.2014.6973492
URL : https://hal.archives-ouvertes.fr/hal-01020180
Subspace Gaussian mixture model for computer-assisted language learning, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5347-5351, 2014. ,
DOI : 10.1109/ICASSP.2014.6854624
Multilingual deep neural network based acoustic modeling for rapid language adaptation, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014. ,
DOI : 10.1109/ICASSP.2014.6855086
URL : http://infoscience.epfl.ch/record/198446/files/Vu_ICASSP_2014.pdf