About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ICASSP 2013
Conference paper
Deep neural network features and semi-supervised training for low resource speech recognition
Abstract
We propose a new technique for training deep neural networks (DNNs) as data-driven feature front-ends for large vocabulary continuous speech recognition (LVCSR) in low resource settings. To circumvent the lack of sufficient training data for acoustic modeling in these scenarios, we use transcribed multilingual data and semi-supervised training to build the proposed feature front-ends. In our experiments, the proposed features provide an absolute improvement of 16% in a low-resource LVCSR setting with only one hour of in-domain training data. While close to three-fourths of these gains come from DNN-based features, the remaining are from semi-supervised training. © 2013 IEEE.