Lightweight Multimodal Edge Computing for Low‐Latency Dialogue Simulation in Spoken English Practice

With the rapid development of intelligent education, spoken English practice systems based on dialogue simulation have become essential tools for language learning. However, traditional cloud‐based dialogue systems face significant challenges in real‐time interaction due to high end‐to‐end latency caused by long‐distance data transmission. To address this, this paper proposes a three‐tier (device‐edge‐cloud) architecture for dynamic computational offloading and a lightweight multimodal edge computing framework (LMecf) that integrates speech, text, and prosodic features using a parameter‐efficient transformer. Additionally, a latency optimization model is introduced to minimize delay while maintaining dialogue quality. Experimental results on the spoken English dialogue corpus (SEDC) show that LMecf reduces latency by compared to cloud‐only systems and compared to edge‐cloud hybrids, thus providing an effective solution for low‐latency, high‐quality spoken English dialogue simulation in intelligent education.