Energy Efficient Speech Command Recognition for Private Smart Home IoT Applications

Christos Zonios, V. Tenentes
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引用次数: 4

Abstract

Speech command recognition (SCR) services for smart homes rely on enterprise cloud servers and a fast internet connection. This negatively impacts service availability in offline use, and it raises privacy concerns in online use. The utilization of high computational resources on Internet-of-Things (IoT) nodes and IoT gateways, allows for traditionally cloud-based services to be implemented closer to the end users. In this paper, we propose a SCR system that consists of an energy-efficient IoT node that communicates over a private BLE home network with an IoT gateway. Feature extraction on sound signals is performed on the IoT node, and speech command classification is performed on the IoT gateway using a novel deep neural network (DNN) model. We evaluate the DNN model accuracy of the proposed system using k-fold cross validation, and tune it based on the effect of different feature extraction parameters on the prediction accuracy, the IoT node energy consumption and system latency. The proposed system performs a task (sound acquisition, feature extraction, transmission and classification) with accuracy higher than 87.8%, latency ∼1.136 sec, and consumes ∼0.87 J of energy on the IoT node per task. The estimated system battery life is more than 422 days when performing a task per minute using a 3.7 V, 2000 mAh battery. The long-term battery life and the small footprint of the proposed system, make it ideal for cable-free discrete smart home installations and for wearable devices, while its offline availability reduces privacy concerns.
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节能语音命令识别用于私人智能家居物联网应用
智能家居的语音命令识别(SCR)服务依赖于企业云服务器和快速互联网连接。这会对离线使用中的服务可用性产生负面影响,并且会引起在线使用中的隐私问题。利用物联网(IoT)节点和物联网网关上的高计算资源,可以使传统的基于云的服务更接近最终用户。在本文中,我们提出了一个由节能物联网节点组成的可控硅系统,该节点通过专用BLE家庭网络与物联网网关进行通信。在物联网节点上对声音信号进行特征提取,并使用新型深度神经网络(DNN)模型在物联网网关上执行语音命令分类。我们使用k-fold交叉验证来评估所提出系统的DNN模型精度,并根据不同特征提取参数对预测精度、物联网节点能耗和系统延迟的影响进行调整。该系统执行一项任务(声音采集、特征提取、传输和分类),准确率高于87.8%,延迟~ 1.136秒,每个任务在物联网节点上消耗约0.87 J的能量。当使用3.7 V, 2000 mAh电池每分钟执行一次任务时,估计系统电池寿命超过422天。该系统的电池寿命长,占地面积小,非常适合无电缆的离散智能家居安装和可穿戴设备,同时其离线可用性减少了对隐私的担忧。
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