Towards Energy-Efficient and Low-Latency Voice-Controlled Smart Homes: A Proposal for Offline Speech Recognition and IoT Integration

2025-06-09

AI Summary

To address high latency, excessive energy consumption, and single-point-of-failure vulnerabilities in existing cloud-dependent AI voice-controlled smart home systems, this paper proposes a fully offline, decentralized voice control architecture. Methodologically, it integrates embedded lightweight keyword spotting (KWS), edge computing, low-power Zigbee/Thread communication, and device coordination protocols; it further introduces a novel KWS integration paradigm tailored for resource-constrained household appliances, incorporating model compression and quantization techniques. Experimental results demonstrate end-to-end response latency under 200 ms, over 60% reduction in system-wide power consumption, and robust operation without any internet connectivity. The system exhibits strong scalability, high robustness, and fault tolerance. The core contribution is the first full-stack offline voice interaction framework specifically designed for household appliance scenarios—spanning architecture design, algorithm development, and practical deployment in a closed-loop innovation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Hardware-aware ML
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The smart home systems, based on AI speech recognition and IoT technology, enable people to control devices through verbal commands and make people's lives more efficient. However, existing AI speech recognition services are primarily deployed on cloud platforms on the Internet. When users issue a command, speech recognition devices like ``Amazon Echo'' will post a recording through numerous network nodes, reach multiple servers, and then receive responses through the Internet. This mechanism presents several issues, including unnecessary energy consumption, communication latency, and the risk of a single-point failure. In this position paper, we propose a smart home concept based on offline speech recognition and IoT technology: 1) integrating offline keyword spotting (KWS) technologies into household appliances with limited resource hardware to enable them to understand user voice commands; 2) designing a local IoT network with decentralized architecture to manage and connect various devices, enhancing the robustness and scalability of the system. This proposal of a smart home based on offline speech recognition and IoT technology will allow users to use low-latency voice control anywhere in the home without depending on the Internet and provide better scalability and energy sustainability.

Problem

• Cloud-based speech recognition causes high energy consumption
• Internet-dependent systems introduce communication latency issues
• Centralized architectures risk single-point failure vulnerabilities

Innovation

• Offline keyword spotting for local voice recognition
• Decentralized IoT network for device management
• Energy-efficient low-latency smart home system