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Most of the existing systems designed for keyword spotting (KWS) rely on a predefined set of keyword phrases. However, the ability to recognize customized keywords is crucial for tailoring interactions with intelligent devices. In this paper, we present a novel framework for customized KWS. This framework leverages the hardware-efficient LiCoNet architecture as the encoder, enhanced by a spectral-temporal pooling layer and a hybrid loss function to facilitate effective word embedding learning. The experimental results on a substantial internal dataset have demonstrated the distinct advantages of the proposed framework. LiCoNet performs at a similar level (1.98% FRR at 0.3 FAs/Hr) to the computationally intensive Conformer, which requires 13x computational resources.

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