TSDNet: An Efficient Light Footprint Keyword Spotting Deep Network Base on Tempera Segment Normalization

Keyword spotting plays a pivotal role in smart devices equipped with speech services, serving as the hands-free interface for the activation of pre-specified tasks. It brings the challenge of maintaining high precision with minimal parameters becomes particularly pronounced in devices constrained by limited hardware resources. In this paper, we delve into the research of Temporal Segment Normalization (TSN) and introduce a highly scalable TSN-based ResNet, termed TSDNet, tailored for deployment on target smart devices. Our approach involves meticulous experimental design, wherein we systematically configure experiments to train distinct scaled models. This not only allows us to validate the effectiveness of TSN but also assesses the performance of the proposed network under varying scales. Experimental results, conducted on the Google Speech Commands dataset v1 and v2, demonstrate that the small, medium, and large-scale models consistently attain stateof-the-art results compared to similar methods. Notably, the smallest model achieves a remarkable 97% accuracy on dataset v1 and 97.1% on dataset v2, while operating with a minimum of 7.4K Bytes of parameters. Furthermore, we undertake retraining of ResNet using our proposed training method, achieving new accuracy margins.