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- W1544623014 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." @default.
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- W1544623014 date "2011-08-27" @default.
- W1544623014 modified "2023-09-25" @default.
- W1544623014 title "Hierarchical tandem features for ASR in Mandarin" @default.
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- W1544623014 doi "https://doi.org/10.21437/interspeech.2011-96" @default.
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