Domain Aware Training for Far-field Small-footprint Keyword Spotting
This addresses performance degradation in keyword spotting for real-life speech applications like smart devices, but it is incremental as it builds on existing convolutional neural network baselines.
The paper tackled the problem of small-footprint keyword spotting in far-field environments, where performance degrades due to noise and reverberation, and proposed domain-aware training methods that maintained close-talking performance while significantly improving far-field accuracy.
In this paper, we focus on the task of small-footprint keyword spotting under the far-field scenario. Far-field environments are commonly encountered in real-life speech applications, causing severe degradation of performance due to room reverberation and various kinds of noises. Our baseline system is built on the convolutional neural network trained with pooled data of both far-field and close-talking speech. To cope with the distortions, we develop three domain aware training systems, including the domain embedding system, the deep CORAL system, and the multi-task learning system. These methods incorporate domain knowledge into network training and improve the performance of the keyword classifier on far-field conditions. Experimental results show that our proposed methods manage to maintain the performance on the close-talking speech and achieve significant improvement on the far-field test set.