L2 Speech

Research on L2 (second language) speech focuses on improving the accuracy and efficiency of automated systems for assessing and assisting L2 learners. Current efforts concentrate on developing and refining machine learning models, including recurrent neural networks (RNNs), large language models (LLMs) like GPT-4, and self-supervised learning models such as Wav2vec 2.0, to address challenges like mispronunciation detection, grammatical error correction, and coherence assessment in both written and spoken L2 language. These advancements aim to provide more effective feedback mechanisms for learners and enhance the capabilities of automatic speech recognition (ASR) systems for non-native speech. The ultimate goal is to create more accurate and helpful tools for language learning and assessment, bridging the performance gap between native and non-native speech processing.