Secure-XPAPF: A Cyber-Secure and Explainable Deep Learning Framework for Automated Pronunciation Assessment and Personalized Feedback in Distance Language Learning

Automated pronunciation assessment has become a core technology for distance foreign language learning, yet existing systems frequently behave as opaque scoring engines and rarely address the cybersecurity risks created by collecting speech, storing learner profiles, generating AI feedback and operating learning analytics at scale. This article proposes Secure-XPAPF, a cyber-secure explainable deep learning framework for automated pronunciation assessment and personalized corrective feedback. The framework integrates automatic speech recognition, pronunciation error detection, pronunciation quality scoring, explainable AI, personalized feedback recommendation, learning analytics, longitudinal progress modeling and a cross-cutting cybersecurity layer. Technically, Secure-XPAPF combines self-supervised speech representations, Transformer-based multi-task pronunciation modeling, phoneme-level error diagnosis, SHAP-style acoustic attribution, attention and saliency visualization, evidence-bound large-language-model feedback, privacy-preserving learning analytics, differential-privacy-aware federated training, adversarial audio robustness testing and zero-trust access governance. Pedagogically, it transforms numerical scores into learner-facing explanations, targeted corrective exercises and longitudinal dashboards for teachers and learners. From a cybersecurity perspective, the framework maps the full attack surface of distance pronunciation learning, including speech data leakage, metadata re-identification, model inversion, membership inference, data poisoning, adversarial audio, prompt injection, insecure LLM output handling and analytics dashboard abuse. The evaluation design combines regression, classification, explanation fidelity, recommendation ranking, learning gain, trust, privacy leakage and security resilience metrics. Illustrative, simulation-calibrated results are provided only as a reproducible reporting template and indicate how a deployed system could compare against GOP, CNN-BiLSTM, Whisper-based and wav2vec2-based baselines. The contribution is a unified, auditable and security-by-design framework that connects speech AI, explainable pedagogy and cyber-resilient learning analytics for high-stakes distance language learning environments.

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