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         author = {Aradilla, Guillermo and Bourlard, Herv{\'{e}} and Magimai.-Doss, Mathew},
       projects = {Idiap},
          title = {Posterior Features Applied to Speech Recognition Tasks with Limited Training Data},
           type = {Idiap-RR},
         number = {Idiap-RR-15-2008},
           year = {2008},
    institution = {IDIAP},
       abstract = {This paper describes an approach where posterior-based features are applied in those recognition tasks where the amount of training data is insufficient to obtain a reliable estimate of the speech variability. A template matching approach is considered in this paper where posterior features are obtained from a MLP trained on an auxiliary database. Thus, the speech variability present in the features is reduced by applying the speech knowledge captured on the auxiliary database. When compared to state-of-the-art systems, this approach outperforms acoustic-based techniques and obtains comparable results to grapheme-based approaches. Moreover, the proposed method can be directly combined with other posterior-based HMM systems. This combination successfully exploits the complementarity between templates and parametric models.},
            pdf = {https://publications.idiap.ch/attachments/reports/2008/aradilla-idiap-rr-08-15.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2008/aradilla-idiap-rr-08-15.ps.gz},