With the rapid development of deep learning techniques, speech-based communication is getting more practically to be embedded into smart devices such as Alexa echo, TV, Fridge, etc. In this work, we have developed an efficient yet accurate Speech Command Recognition (SCR), that is particularly appropriate for low-resource devices. To this aim, a novel neural network, called Light Interior Search Network (LIS-Net), is presented that works with raw speech signal. LIS-Net is structurally composed of a sequence of parameterized LIS-Blocks, each of which is a stack of LIS-Cores, exploring the feature-map inheritance to learn highly distinctive and lightweight footprint of speech patterns. The proposed network is validated on Google Speech Commands benchmark speech datasets, demonstrating a significant improvement of accuracy and processing time in comparison with other state-of-the-art techniques.

随着深度学习技术的飞速发展，基于语音的通信已越来越实用地嵌入到智能设备中，例如Alexa回声，电视，冰箱等。在这项工作中，我们开发了一种高效而准确的语音命令识别（SCR） ），特别适合资源不足的设备。为了达到这个目的，提出了一种新型的神经网络，称为Light Interior Search Network（LIS-Net），它可以处理原始语音信号。LIS-Net在结构上由一系列参数化的LIS块组成，每个LIS块都是LIS核心的堆栈，探索特征图继承以学习高度独特且轻巧的语音模式。拟议的网络已在Google Speech Commands基准语音数据集中进行了验证，