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Most existing keyword spotting research focuses on conditions with slight ormoderate noise. In this paper, we try to tackle a more challenging task:detecting keywords buried under strong interfering speech (10 times higher thanthe keyword in amplitude), and even worse, mixed with other keywords. Wepropose a novel Mix Training (MT) strategy that encourages the model todiscover low-energy keywords from noisy and mixed speech. Experiments wereconducted with a vanilla CNN and two EfficientNet (B0/B2) architectures. Theresults evaluated with the Google Speech Command dataset demonstrated that theproposed mix training approach is highly effective and outperforms standarddata augmentation and mixup training.

Spot keywords from very noisy and mixed speech