A TRANSFER LEARNING AND PROGRESSIVE STACKING APPROACH TO REDUCING DEEP MODEL SIZES WITH AN APPLICATION TO SPEECH ENHANCEMENT

Leveraging upon transfer learning, we distill the knowledge in a conventional wide and deep neural network (DNN) into a narrower yet deeper model with fewer parameters and comparable system performance for speech enhancement. We present three transfer-learning solutions to accomplish our goal. First, the knowledge embedded in the form of the output values of a high-performance DNN is used to guide the training of a smaller DNN model in sequential transfer learning. In the second multi-task transfer learning solution, the smaller DNN is trained to learn the output value of the larger DNN, and the speech enhancement task in parallel. Finally, a progressive stacking transfer learning is accomplished through multi-task learning, and DNN stacking. Our experimental evidences demonstrate 5 times parameter reduction while maintaining similar enhancement performance with the proposed framework.