  In this study, we propose a novel adversarial reprogramming (AR) approach for
low-resource spoken command recognition (SCR), and build an AR-SCR system. The
AR procedure aims to modify the acoustic signals (from the target domain) to
repurpose a pretrained SCR model (from the source domain). To solve the label
mismatches between source and target domains, and further improve the stability
of AR, we propose a novel similarity-based label mapping technique to align
classes. In addition, the transfer learning (TL) technique is combined with the
original AR process to improve the model adaptation capability. We evaluate the
proposed AR-SCR system on three low-resource SCR datasets, including Arabic,
Lithuanian, and dysarthric Mandarin speech. Experimental results show that with
a pretrained AM trained on a large-scale English dataset, the proposed AR-SCR
system outperforms the current state-of-the-art results on Arabic and
Lithuanian speech commands datasets, with only a limited amount of training
data.
