From Speech Recognition to Communicative Readiness: an NLP-Driven Adaptive Pronunciation Framework for Distance Foreign-Language Learning and the Social Reintegration of Convicted Individuals

Automatic speech recognition (ASR) is increasingly used in computer-assisted pronunciation training, but successful lexical recognition is not a valid substitute for pronunciation assessment. This article synthesizes evidence on ASR effectiveness, CAPT design, recognition-assessment conflict, peer-supported practice, and recent automatic pronunciation assessment (APA), and converts it into an NLP-driven adaptive framework. The model separates transcription, segmental evidence, prosody, fluency, human-calibrated intelligibility, semantic task completion, uncertainty, and pedagogical decision making. It combines posterior-based phone scoring, reliability-weighted multi-view aggregation, calibrated uncertainty, feedback prioritization, longitudinal learner-state updating, controlled-to-spontaneous transfer, and expected-learning-gain task selection. High ASR confidence cannot by itself produce a high pronunciation decision, while noisy or out-of-distribution speech can trigger abstention rather than learner penalty. The framework is intended for general distance foreign-language learning and includes a non-punitive deployment layer for convicted or incarcerated adults, where speech scores remain educational signals and are not treated as measures of rehabilitation or recidivism. A validation plan is specified using human agreement, error-detection F1, calibration, selective risk, fairness gaps, and longitudinal learning effects.

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