Improving Mispronunciation Detection with Wav2vec2-based Momentum Pseudo-Labeling for Accentedness and Intelligibility Assessment. (arXiv:2203.15937v2 [eess.AS] UPDATED)

Current leading mispronunciation detection and diagnosis (MDD) systems
achieve promising performance via end-to-end phoneme recognition. One challenge
of such end-to-end solutions is the scarcity of human-annotated phonemes on
natural L2 speech. In this work, we leverage unlabeled L2 speech via a
pseudo-labeling (PL) procedure and extend the fine-tuning approach based on
pre-trained self-supervised learning (SSL) models. Specifically, we use Wav2vec
2.0 as our SSL model, and fine-tune it using original labeled L2 speech samples
plus the created pseudo-labeled L2 speech …

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