%Aigaion2 BibTeX export from Idiap Publications
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@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}
}