Detecting pronunciation errors is a critical step in developing second-language speaking proficiency. Traditional evaluation methods rely on teacher judgment or peer feedback, which can be subjective and inconsistent. With emerging artificial intelligence (AI) technologies such as automatic speech recognition (ASR) and machine learning models, pronunciation error detection has shifted toward more objective, data-driven approaches. This article examines how AI algorithms identify and categorize pronunciation errors, enhance diagnostic precision, and support personalized learning pathways. Recent research reveals that AI-supported systems can detect subtle phonological deviations and provide real-time corrective feedback, representing a major pedagogical advancement over traditional assessment practices.

Artificial Intelligence, Pronunciation Error Detection, Speech Recognition, Machine Learning, Computer-Assisted Pronunciation Training (CAPT), ESL/EFL Learners

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