Lightweight dynamic filter for keyword spotting
Keyword Spotting (KWS) from speech signal is widely applied for being fully hands free speech recognition. The KWS network is designed as a small footprint model to be constantly monitored. Recently, dynamic filter based models are applied in deep learning applications to enhance a system's robustness or accuracy. However, as a dynamic filter framework requires high computational cost, the usage is limited to the condition of the device. In this paper, we proposed a lightweight dynamic filter to improve the performance of KWS. Our proposed model divides dynamic filter as two branches to reduce the computational complexity. This lightweight dynamic filter is applied to the front-end of KWS to enhance the separability of the input data. The experiments show that our model is robustly working on unseen noise and small training data environment by using small computational resource.
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