- Linchen Zhu, A. Waibel, T. Asfour, Sebastian Stüker. 2017. Neural Network-based Small-Footprint Flexible Keyword Spotting. Abstract: Real-time keyword spotting (KWS) on mobile devices requires a small memory footprint, low latency, and low computational cost. Conventional approaches such as HMM-based KWS do not fulfill these requirements, as Viterbi decoding tends to lead to high computational cost. Therefore, this work proposes a neural network-based small-footprint flexible KWS system appropriate for mobile devices. Our novel KWS system is composed of three modules: the feature extraction module, the posterior probability estimation module, and the posterior handling module. The feature extraction module produces acoustic features from the input audio stream, while the posterior probability estimation module generates posterior probabilities for subword units such as phonemes and senones using various types of neural networks including feedforward neural networks and recurrent neural networks such as LSTM, TDNN-LSTM, and so on. Finally, the posterior handling module calculates final confidence scores for the predefined keywords based on the posteriors from networks. Experiments have been performed to prove the effectiveness of this KWS approach. Experimental results on neural network training show that RNNs such as LSTM, TDNN-LSTM, etc., outperform FFNNs significantly on framewise phoneme/senone classification. Then, the KWS system is built based on the trained networks, and the performance is evaluated by detecting the predefined key-phrase ”okay cosa”. Evaluation results demonstrate that this KWS system achieves lower computational cost and a smaller memory footprint compared with conventional KWS approaches, and therefore makes a step towards real-time KWS on mobile devices.
