Mispronunciation Detection

Mispronunciation detection research aims to automatically identify and diagnose pronunciation errors in speech, primarily to improve computer-assisted language learning and speech therapy. Current efforts focus on developing robust and efficient models, often employing deep learning architectures like recurrent neural networks (RNNs), transformers, and connectionist temporal classification (CTC), frequently incorporating self-supervised learning and multi-task learning to address data scarcity issues. These advancements hold significant promise for creating more effective language learning tools and personalized speech therapy interventions, ultimately improving pronunciation accuracy and fluency for non-native speakers and individuals with speech disorders.