Mispronunciation Detection and Diagnosis Without Model Training: A Retrieval-Based Approach

A novel framework for Mispronunciation Detection and Diagnosis (MDD) has been proposed, utilizing retrieval techniques with a pretrained Automatic Speech Recognition (ASR) model, eliminating the need for model training. This approach demonstrated a superior F1 score of 69.60% on the L2-ARCTIC dataset, showcasing its effectiveness in identifying pronunciation errors without the complexities of traditional methods.
This development is significant as it simplifies the process of MDD, making it more accessible for language learners and speech therapists. By avoiding the need for phoneme-specific modeling or additional training, the framework can be implemented more efficiently, potentially enhancing language acquisition and therapy outcomes.
The advancement in retrieval-based methods for MDD aligns with ongoing research in ASR, particularly in contexts such as endangered language learning and clinical dialogue assessment. These studies emphasize the importance of improving ASR systems to better serve diverse linguistic needs and address limitations in traditional evaluation metrics, highlighting a broader trend towards more inclusive and effective language technologies.

Mispronunciation Detection and Diagnosis Without Model Training: A Retrieval-Based Approach