Time-delayed bottleneck highway networks using a dft feature for keyword spotting

Ming Sun

Abstract: This paper presents a novel deep neural network (DNN) architecture with highway blocks (HWs) using a complex discrete Fourier transform (DFT) feature for keyword spotting. In our previous work, we showed that the feed-forward DNN with a time-delayed bottleneck layer (TDB-DNN) directly trained from the audio input outperformed the model with the log-mel filter bank energy feature (LFBE), given a large amount of training data [1]. However, the deeper structure of such an audio input DNN makes an optimization problem more difficult, which could easily fall in one of the local minimum solutions. In order to alleviate the problem, we propose a new HW network with a time-delayed bottleneck layer (TDB-HW). Our TDB-HW networks can learn a bottleneck feature representation through optimization based on the cross-entropy criterion without stage-wise training proposed in [1]. Moreover, we use the complex DFT …