Small-Footprint Magic Word Detection Method Using Convolutional LSTM Neural Network

The number of consumer devices which can be operated by voice is increasing every year. Magic Word Detection (MWD), the detection of an activation keyword in continuous speech, has become an essential technology for the hands-free operation of such devices. Because MWD systems need to run constantly in order to detect Magic Words at any time, many studies have focused on the development of a small-footprint system. In this paper, we propose a novel, small-footprintMWDmethod which uses a convolutional Long Short-Term Memory (LSTM) neural network to capture frequency and time domain features over time. As a result, the proposed method outperforms the baseline method while reducing the number of parameters by more than 80%. An experiment on a small-scale device demonstrates that our model is efficient enough to function in real time.