%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Dighe_ICASSP_2016,
         author = {Dighe, Pranay and Luyet, Gil and Asaei, Afsaneh and Bourlard, Herv{\'{e}}},
       projects = {Idiap, PHASER 200021-153507},
          month = mar,
          title = {Exploiting Low-dimensional Structures to Enhance DNN based Acoustic Modeling in Speech Recognition},
      booktitle = {Proceedings of 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016)},
           year = {2016},
          pages = {5690-5694},
      publisher = {IEEE},
       location = {Shanghai},
       abstract = {We propose to model the acoustic space of deep neural network
(DNN) class-conditional posterior probabilities as a union of low-
dimensional subspaces. To that end, the training posteriors are used
for dictionary learning and sparse coding. Sparse representation of
the test posteriors using this dictionary enables projection to the
space of training data. Relying on the fact that the intrinsic di-
mensions of the posterior subspaces are indeed very small and the
matrix of all posteriors belonging to a class has a very low rank,
we demonstrate how low-dimensional structures enable further en-
hancement of the posteriors and rectify the spurious errors due to
mismatch conditions. The enhanced acoustic modeling method leads
to improvements in continuous speech recognition task using hybrid
DNN-HMM (hidden Markov model) framework in both clean and
noisy conditions, where upto 15.4\% relative reduction in word error
rate (WER) is achieved.},
            pdf = {https://publications.idiap.ch/attachments/papers/2016/Dighe_ICASSP_2016.pdf}
}