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

Authors: Tong Wang; Xuesong Gao; Lingfei Wang; Man Zhang

Addresses: Academic Affairs Office, Changchun Normal College, Changchun, 130216, China ' School of Design, Jilin Animation Institute, Changchun, 130012, China ' School of Arts, University of Science and Technology Liaoning, Anshan, 114051, China ' School of Civil Engineering, University of Science and Technology Liaoning, Anshan, 114051, China

Abstract: 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.

Keywords: pronunciation feedback; speech recognition; wireless sensor network; WSN; time-division multiple access protocol; domain adversarial training algorithm.

DOI: 10.1504/IJSNET.2026.153838

International Journal of Sensor Networks, 2026 Vol.51 No.1, pp.1 - 15

Received: 11 Nov 2025
Accepted: 15 Nov 2025
Published online: 27 May 2026 *

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