A DEEP NEURAL NETWORK INTEGRATED WITH FILTERBANK LEARNING FOR SPEECH RECOGNITION

Deep neural networks (DNN) have achieved significant success in the field of speech recognition. One of the main advantages of the DNN is automatic feature extraction without human intervention. Therefore, we incorporate a pseudo-filterbank layer to the bottom of DNN and train the whole filterbank layer and the following networks jointly, while most systems take pre-defined mel-scale filterbanks as acoustic features to DNN. In the experiment, we use Gaussian functions instead of triangular mel-scale filterbanks. This technique enables a filterbank layer to maintain the functionality of frequency domain smoothing. The proposed method provides an 8.0% relative improvement in clean condition on ASJ+JNAS corpus and a 2.7% relative improvement on noise-corrupted ASJ+JNAS corpus compared with traditional fully-connected DNN. Experimental results show that the frame-level transformation of filterbank layer constrains flexibility and promotes learning efficiency in acoustic modeling.