Hierarchical Tandem Features for ASR in Mandarin
| Type of publication: | Idiap-RR |
| Citation: | Pinto_Idiap-RR-39-2010 |
| Number: | Idiap-RR-39-2010 |
| Year: | 2010 |
| Month: | 11 |
| 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. |
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| Projects |
Idiap SNSF-KEYSPOT IM2 |
| Authors | |
| Crossref by |
Pinto_INTERSPEECH_2011 |
| Added by: | [ADM] |
| Total mark: | 0 |
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