Temporal Feedback Convolutional Recurrent Neural Networks for Keyword Spotting

While end-to-end learning has become a trend in deep learning, the model architecture is often designed to incorporate domain knowledge. We propose a novel convolutional recurrent neural network (CRNN) architecture with temporal feedback connections, inspired by the feedback pathways from the brain to ears in the human auditory system. The proposed architecture uses a hidden state of the RNN module at the previous time to control the sensitivity of channel-wise feature activations in the CNN blocks at the current time, which is analogous to the mechanism of the outer hair-cell. We apply the proposed model to keyword spotting where the speech commands have sequential nature. We show the proposed model consistently outperforms the compared model without temporal feedback for different input/output settings in the CRNN framework. We also investigate the details of the performance improvement by conducting a failure analysis of the keyword spotting task and a visualization of the channel-wise feature scaling in each CNN block.