Cognitive Load Mapping in L۲ Pronunciation Using Machine Learning

This conceptual paper proposes a framework for mapping potential cognitive pressure during second‑language (L۲) pronunciation by integrating acoustic analysis and machine‑learning techniques. Drawing on Cognitive Load Theory, the study argues that pronunciation difficulty is not a fixed property of phonemes but a dynamic, learner‑specific phenomenon reflected in behavioral patterns such as hesitation, pause duration, speech timing, intensity variation, and voice‑quality changes. The framework consists of a multi‑stage process in which learner speech is captured, acoustic features are extracted, and machine‑learning models identify segments that may indicate increased cognitive demand. These segments are then aligned with phonemes, syllables, or consonant clusters to generate an individualized “cognitive‑pressure map.” As a conceptual contribution, the paper highlights the potential of combining cognitive theory with speech‑technology tools to move beyond accuracy‑based pronunciation assessment toward adaptive, learner‑specific instruction. The proposed model suggests that identifying processing‑pressure points may help teachers and learning platforms adjust task difficulty, personalize pronunciation practice, and provide more targeted feedback. While the framework offers a promising direction for future research, its effectiveness depends on empirical validation of acoustic indicators, model reliability, and the impact of cognitive‑pressure‑informed instruction on L۲ pronunciation outcomes.

کلیدواژه ها:

Cognitive Load ، L۲ Pronunciation ، Machine Learning ، Acoustic Analysis ، Speech Technology ، Personalized Learning ، Pronunciation Assessment