%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Ram_INTERSPEECH_2015,
         author = {Ram, Dhananjay and Asaei, Afsaneh and Dighe, Pranay and Bourlard, Herv{\'{e}}},
       projects = {Idiap, PHASER 200021-153507},
          month = sep,
          title = {Sparse Modeling of Posterior Exemplars for Keyword Detection},
      booktitle = {Proceedings of Interspeech},
           year = {2015},
          pages = {3690-3694},
       abstract = {Sparse representation has been shown to be a powerful modeling framework for classification and detection tasks. In this paper, we propose a new keyword detection algorithm based on sparse representation of the posterior exemplars. The posterior exemplars are phone conditional probabilities obtained from a deep neural network. This method relies on the concept that a keyword exemplar lies in a low-dimensional subspace which can be represented as a sparse linear combination of the training exemplars. The training exemplars are used to learn a dictionary for sparse representation of the keywords and background classes. Given this dictionary, the sparse representation of a test exemplar is used to detect the keywords. The experimental results demonstrate the potential of the proposed sparse modeling approach and it compares favorably with the state-of-the-art HMM-based framework on Numbers'95 database.},
            pdf = {https://publications.idiap.ch/attachments/papers/2015/Ram_INTERSPEECH_2015.pdf}
}