Efficient knowledge distillation from an ensemble of teachers

Abstract

This paper describes the effectiveness of knowledge distillation using teacher student training for building accurate and compact neural networks. We show that with knowledge distillation, information from multiple acoustic models like very deep VGG networks and Long Short-Term Memory (LSTM) models can be used to train standard convolutional neural network (CNN) acoustic models for a variety of systems requiring a quick turnaround. We examine two strategies to leverage multiple teacher labels for training student models. In the first technique, the weights of the student model are updated by switching teacher labels at the minibatch level. In the second method, student models are trained on multiple streams of information from various teacher distributions via data augmentation. We show that standard CNN acoustic models can achieve comparable recognition accuracy with much smaller number of model parameters compared to teacher VGG and LSTM acoustic models. Additionally we also investigate the effectiveness of using broadband teacher labels as privileged knowledge for training better narrowband acoustic models within this framework. We show the benefit of this simple technique by training narrowband student models with broadband teacher soft labels on the Aurora 4 task.