Feature Representation Learning In Deep Neural Networks
AUTOMATIC SPEECH RECOGNITION: A DEEP LEARNING APPROACH(2015)
摘要
In this chapter, we show that deep neural networks jointly learn the feature representation and the classifier. Through many layers of nonlinear processing, DNNs transform the raw input feature to a more invariant and discriminative representation that can be better classified by the log-linear model. In addition, DNNs learn a hierarchy of features. The lower-level features typically catch local patterns. These patterns are very sensitive to changes in the raw feature. The higher-level features, however, are built upon the low-level features and are more abstract and invariant to the variations in the raw feature. We demonstrate that the learned high-level features are robust to speaker and environment variations.
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