THEROLEOFAIALGORITHMSINDETECTINGSTUDENTS'PRONUNCIATIONERRORS

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.发音错误检测是提升第二语言口语能力的关键环节。传统评估方法多依赖教师评判或同伴互评，这类方式往往主观性较强且一致性不足。随着自动语音识别（Automatic Speech Recognition, ASR）与机器学习模型等新兴人工智能（Artificial Intelligence, AI）技术的发展，发音错误检测正转向更为客观、数据驱动的研究路径。本文探讨了人工智能算法如何识别并归类发音错误、提升诊断精度，以及为个性化学习路径提供支撑。近期研究表明，依托人工智能的系统能够检测出细微的音系偏差，并提供实时纠错反馈，相较传统评估实践实现了教学方法层面的重大进步。