Few-Shot Keyword Spotting from Mixed Speech

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.