Despite showing state-of-the-art performance, deep learning for speech
recognition remains challenging to deploy in on-device edge scenarios such as
mobile and other consumer devices. Recently, there have been greater efforts in
the design of small, low-footprint deep neural networks (DNNs) that are more
appropriate for edge devices, with much of the focus on design principles for
hand-crafting efficient network architectures. In this study, we explore a
human-machine collaborative design strategy for building low-footprint DNN
architectures for speech recognition through a marriage of human-driven
principled network design prototyping and machine-driven design exploration.
The efficacy of this design strategy is demonstrated through the design of a
family of highly-efficient DNNs (nicknamed EdgeSpeechNets) for
limited-vocabulary speech recognition. Experimental results using the Google
Speech Commands dataset for limited-vocabulary speech recognition showed that
EdgeSpeechNets have higher accuracies than state-of-the-art DNNs (with the best
EdgeSpeechNet achieving ~97% accuracy), while achieving significantly smaller
network sizes (as much as 7.8x smaller) and lower computational cost (as much
as 36x fewer multiply-add operations, 10x lower prediction latency, and 16x
smaller memory footprint on a Motorola Moto E phone), making them very
well-suited for on-device edge voice interface applications.

本文探讨一种人机协同设计策略，通过人驱动的设计原则网络设计原型和机器驱动的设计探索来构建语音识别的低存储深度神经网络体系结构，实验证明此设计策略可以构建出一系列高效率的 DNNs，用于有限词汇的语音识别，更高的精度与更小的网络体积以及更低的计算成本使它们非常适合设备上的语音接口应用。