Spot keywords from very noisy and mixed speech
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.
PDF Abstract
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Convolution •
Dense Connections •
Depthwise Convolution •
Depthwise Separable Convolution •
Dropout •
EfficientNet •
Inverted Residual Block •
Mixup •
Pointwise Convolution •
ReLU •
RMSProp •
Sigmoid Activation •
Squeeze-and-Excitation Block •
Swish
