Real-time pronunciation feedback system for language learning integrating speech recognition and sensor networks

In language learning, real-time pronunciation feedback relies on stable, high-quality speech data streams. However, traditional speech transmission methods are prone to latency, severely impacting the learning experience. To address this, this paper proposes utilising wireless sensor networks as the primary backbone for speech transmission, combined with speech recognition to construct a real-time pronunciation feedback system. Addressing the prolonged latency of conventional time-division multiple access protocols, this paper introduces a variable frame length optimisation strategy. By limiting the maximum frame duration, network transmission delays are effectively reduced. Upon acquiring high-quality speech data, a pronunciation diagnosis feedback module is constructed based on the Conformer model. Employing a two-stage domain adversarial training approach enables the model to overcome accent interference and accurately diagnose pronunciation errors. Experiments demonstrate that the system maintains an end-to-end average latency below 9.4 ms and achieves a pronunciation error rate as low as 3.23%, providing an effective technical pathway to overcome interaction bottlenecks in online language learning.