  Most existing keyword spotting research focuses on conditions with slight or
moderate noise. In this paper, we try to tackle a more challenging task:
detecting keywords buried under strong interfering speech (10 times higher than
the keyword in amplitude), and even worse, mixed with other keywords. We
propose a novel Mix Training (MT) strategy that encourages the model to
discover low-energy keywords from noisy and mixed speech. Experiments were
conducted with a vanilla CNN and two EfficientNet (B0/B2) architectures. The
results evaluated with the Google Speech Command dataset demonstrated that the
proposed mix training approach is highly effective and outperforms standard
data augmentation and mixup training.
