This article examines t he impact of phonetic variability on the accuracy of speech recognition systems in second language (L2) learners. It delves into how variations in pronunciation—stemming from regional accents, individual speech patterns, and language proficiency—affect the performance of automatic speech recognition (ASR) technologies. The study highlights the challenges faced by ASR systems in accurately transcribing non-native speech and discusses the implications for language learning applications. By analyzing current research and technological advancements, the article offers insights into improving ASR systems to better accommodate the diverse phonetic profiles of L2 speakers.

phonetic variability, speech recognition, second language learners, automatic speech recognition, pronunciation accuracy, language proficiency, regional accents, ASR technology

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