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

Abstract

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.