%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{Dighe_Idiap-RR-19-2016,
         author = {Dighe, Pranay and Asaei, Afsaneh and Bourlard, Herv{\'{e}}},
       projects = {Idiap, PHASER 200021-153507},
          month = {8},
          title = {Sparse Hidden Markov Models for Exemplar-based Speech Recognition Using Deep Neural Network Posterior Features},
           type = {Idiap-RR},
         number = {Idiap-RR-19-2016},
           year = {2016},
    institution = {Idiap},
       abstract = {Statistical speech recognition has been cast as a natural realization of the compressive sensing problem in this work. The compressed acoustic observations are sub-word posterior probabilities obtained from a deep neural network. Dictionary learning and sparse recovery are exploited for inference of the high-dimensional sparse word posterior probabilities. This formulation amounts to realization of a \textit{sparse} hidden Markov model where each state is characterized by a dictionary learned from training exemplars and the emission probabilities are obtained from sparse representations of test exemplars. This new dictionary-based speech processing paradigm alleviates the need for a huge collection of exemplars as required in the conventional exemplar-based methods. We study the performance of the proposed approach for continuous speech recognition using Phonebook and Numbers'95 database.},
            pdf = {https://publications.idiap.ch/attachments/reports/2015/Dighe_Idiap-RR-19-2016.pdf}
}