Learning Self-Informed Feature Contribution for Deep Learning-Based Acoustic Modeling

In this paper, we introduce a new feature engineering approach for deep learning-based acoustic modeling, which utilizes input feature contributions. For this purpose, we propose an auxiliary deep neural network (DNN) called a feature contribution network (FCN) whose output layer is composed of sigmoid-based contribution gates. In our framework, the FCN tries to learn element-level discriminative contributions of input features and an acoustic model network (AMN) is trained by gated features generated by element-wise multiplication between contribution gate outputs and input features. In addition, we also propose a regularization method for the FCN, which helps the FCN to activate the minimum number of the gates. The proposed methods were evaluated on the TED-LIUM release 1 corpus. We applied the proposed methods to DNN- and long short-term memory-based AMNs. Experimental results results showed that AMNs with the FCNs consistently improved recognition performance compared with AMN-only frameworks.