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Improving Mispronunciation Detection with Wav2vec2-based Momentum Pseudo-Labeling for Accentedness and Intelligibility Assessment [article]

Mu Yang, Kevin Hirschi, Stephen D. Looney, Okim Kang, John H. L. Hansen
2022 arXiv   pre-print
In addition, we conduct an open test on a separate UTD-4Accents dataset, where our system recognition outputs show a strong correlation with human perception, based on accentedness and intelligibility.  ...  We show that fine-tuning with pseudo labels achieves a 5.35% phoneme error rate reduction and 2.48% MDD F1 score improvement over a labeled-samples-only fine-tuning baseline.  ...  In this study, we focus on detecting phonetic-level pronunciation errors for L2 speech intelligibility and accentedness assessment.  ... 
arXiv:2203.15937v3 fatcat:itrevkthgrbklk6ha27iysgpta