Few-Shot Keyword Spotting from Mixed Speech
<|reference_start|>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.<|reference_end|>
@article{yuan2024few-shot,
    title={Few-Shot Keyword Spotting from Mixed Speech},
    author={Junming Yuan, Ying Shi, LanTian Li, Dong Wang, Askar Hamdulla},
    journal={arXiv preprint arXiv:2407.06078},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06078},
    primaryClass={cs.SD}
}