%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Pinto_INTERSPEECH_2011,
         author = {Pinto, Joel and Magimai.-Doss, Mathew and Bourlard, Herv{\'{e}}},
       projects = {Idiap, IM2},
          title = {Hierarchical Tandem Features for ASR in Mandarin},
      booktitle = {Proceedings of Interspeech},
           year = {2011},
       crossref = {Pinto_Idiap-RR-39-2010}
}



crossreferenced publications: 
@TECHREPORT{Pinto_Idiap-RR-39-2010,
         author = {Pinto, Joel Praveen and Magimai.-Doss, Mathew and Bourlard, Herv{\'{e}}},
       projects = {Idiap, SNSF-KEYSPOT, IM2},
          month = {11},
          title = {Hierarchical Tandem Features for ASR in Mandarin},
           type = {Idiap-RR},
         number = {Idiap-RR-39-2010},
           year = {2010},
    institution = {Idiap},
       abstract = {We apply multilayer perceptron (MLP) based hierarchical Tandem
features to large vocabulary continuous speech recognition in Mandarin.
Hierarchical Tandem features are estimated using a cascade
of two MLP classifiers which are trained independently. The first
classifier is trained on perceptual linear predictive coefficients with
a 90 ms temporal context. The second classifier is trained using the
phonetic class conditional probabilities estimated by the first MLP,
but with a relatively longer temporal context of about 150 ms. Experiments
on the Mandarin DARPA GALE eval06 dataset show significant
reduction (about 7.6\% relative) in character error rates by using
hierarchical Tandem features over conventional Tandem features.},
            pdf = {https://publications.idiap.ch/attachments/reports/2010/Pinto_Idiap-RR-39-2010.pdf}
}