Cross-Domain Few-Shot Open-Set Keyword Spotting Using Keyword Adaptation and Prototype Reprojection

Personalized keyword spotting (KWS) with few enrollment utterances remains an important problem over years. KWS remains a challenging task due to the following factors, including the scarcity of enrollment samples, speech variation in the open-set scenarios, and distributional gap between source and target domains. In this paper, we formulate a KWS task of Cross-Domain Few-Shot Open-Set (CD-FSOS) and propose a dedicated framework Adapt-KWS to bridge the distribution gap between the source domain and target open-set domain with quite limited enrollment data. The proposed Adapt-KWS consists of a set of Custom-Keyword Adapters (CKAs) and a Prototype Reprojection Module (PRM). CKAs enable the efficient adaptation to new target tasks with limited training samples, aiming to improve cross-domain generalization. PRM reprojects the support prototypes into the query embedding space to enhance their alignment, mitigating the potential covariate shift between open-set queries and enrollments. Experimental results demonstrate the effectiveness of our framework and proposed modules on multiple datasets. Code will be available at: https://github.com/Raynaming/CD-FSOS-KWS.