Few-shot Keyword-incremental Learning Using Compositional Information

Abstract

Recognizing not only pre-defined keywords but also continuously expanding new keywords, often with limited data, has emerged as a main problem in recent keyword spotting research. To address this challenge, few-shot class-incremental learning approaches have gained attention, initially training models on sufficient data in a base session and then continuously adapting to recognize new classes with limited data. Recent focus has been on prototype-based calibration, which fuses new prototypes with weighted base prototypes. However, this method risks misclassification due to increased similarity between new and base classes. To mitigate this issue, we propose a compositional feature-based calibration method. Instead of directly using base prototypes, our approach extracts and utilizes rich compositional information from the initial session to enhance new class representations. Experimental results on two keyword spotting datasets demonstrate the superiority of our proposed method, showing improved performance in recognizing initial and new keywords.