Hierarchical tandem features for ASR in Mandarin
Joel Pinto, Mathew Magimai.-Doss, Hervé A. Bourlard · 2011
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.