A Comparative Study of ASR Implementations in Resource-Constrained Wireless Sensor Networks for Real-Time Voice Communication

TL;DR

This paper compares three ASR implementation strategies in resource-constrained wireless sensor networks, analyzing their trade-offs in bandwidth, latency, accuracy, and adaptability for real-time voice communication.

Contribution

It provides a comprehensive comparison of NSR, DSR, and ESR approaches, offering insights for selecting suitable ASR methods in limited-resource WSNs.

Findings

NSR has high bandwidth consumption but good accuracy.

DSR balances processing load and communication overhead.

ESR offers low latency and energy efficiency.

Abstract

This paper investigates the challenges and trade-offs associated with implementing Automatic Speech Recognition (ASR) in resource-limited Wireless Sensor Networks (WSNs) for real-time voice communication. We analyze three main architectural approaches: Network Speech Recognition (NSR), Distributed Speech Recognition (DSR), and Embedded Speech Recognition (ESR). Each approach is evaluated based on factors such as bandwidth consumption, processing power requirements, latency, accuracy (Word Error Rate - WER), and adaptability to offline operation. We discuss the advantages and disadvantages of each method, considering the computational and communication limitations of WSN nodes. This comparative study provides insights for selecting the most appropriate ASR implementation strategy based on specific application requirements and resource constraints.