  In this paper, we investigated a speech augmentation based unsupervised
learning approach for keyword spotting (KWS) task. KWS is a useful speech
application, yet also heavily depends on the labeled data. We designed a
CNN-Attention architecture to conduct the KWS task. CNN layers focus on the
local acoustic features, and attention layers model the long-time dependency.
To improve the robustness of KWS model, we also proposed an unsupervised
learning method. The unsupervised loss is based on the similarity between the
original and augmented speech features, as well as the audio reconstructing
information. Two speech augmentation methods are explored in the unsupervised
learning: speed and intensity. The experiments on Google Speech Commands V2
Dataset demonstrated that our CNN-Attention model has competitive results.
Moreover, the augmentation based unsupervised learning could further improve
the classification accuracy of KWS task. In our experiments, with augmentation
based unsupervised learning, our KWS model achieves better performance than
other unsupervised methods, such as CPC, APC, and MPC.
