%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Asaei_ICASSP_2010,
         author = {Asaei, Afsaneh and Picart, Benjamin and Bourlard, Herv{\'{e}}},
       projects = {Idiap},
          title = {Analysis of Phone Posterior Feature Space Exploiting Class Specific Sparsity and MLP-based Similarity Measure},
      booktitle = {2010 IEEE International Conference on Acoustics, Speech and Signal Processing},
           year = {2010},
       abstract = {Class posterior distributions have recently been used quite
successfully in Automatic Speech Recognition (ASR), either
for frame or phone level classification or as acoustic
features, which can be further exploited (usually after some
"ad hoc" transformations) in different classifiers (e.g., in
Gaussian Mixture based HMMs). In the present paper, we
show preliminary results showing that it may be possible to
perform speech recognition without explicit subword unit
(phone) classification or likelihood estimation, simply
answering the question whether two acoustic (posterior)
vectors belong to the same subword unit class or not. In this
paper, we first exhibit specific properties of the posterior
acoustic space before showing how those properties can be
exploited to reach very high performance in deciding (based
on an appropriate, trained, distance metric, and hypothesis
testing approaches) whether two posterior vectors belong to
the same class or not. Performance as high as 90\% correct
decision rates are reported on the TIMIT database, before
reporting kNN phone classification rates.},
            pdf = {https://publications.idiap.ch/attachments/papers/2010/Asaei_ICASSP_2010.pdf}
}