  Few-shot keyword spotting (KWS) aims to detect unknown keywords with limited
training samples. A commonly used approach is the pre-training and fine-tuning
framework. While effective in clean conditions, this approach struggles with
mixed keyword spotting -- simultaneously detecting multiple keywords blended in
an utterance, which is crucial in real-world applications. Previous research
has proposed a Mix-Training (MT) approach to solve the problem, however, it has
never been tested in the few-shot scenario. In this paper, we investigate the
possibility of using MT and other relevant methods to solve the two practical
challenges together: few-shot and mixed speech. Experiments conducted on the
LibriSpeech and Google Speech Command corpora demonstrate that MT is highly
effective on this task when employed in either the pre-training phase or the
fine-tuning phase. Moreover, combining SSL-based large-scale pre-training
(HuBert) and MT fine-tuning yields very strong results in all the test
conditions.
