  Automated speaking assessment (ASA) typically involves automatic speech
recognition (ASR) and hand-crafted feature extraction from the ASR transcript
of a learner's speech. Recently, self-supervised learning (SSL) has shown
stellar performance compared to traditional methods. However, SSL-based ASA
systems are faced with at least three data-related challenges: limited
annotated data, uneven distribution of learner proficiency levels and
non-uniform score intervals between different CEFR proficiency levels. To
address these challenges, we explore the use of two novel modeling strategies:
metric-based classification and loss reweighting, leveraging distinct SSL-based
embedding features. Extensive experimental results on the ICNALE benchmark
dataset suggest that our approach can outperform existing strong baselines by a
sizable margin, achieving a significant improvement of more than 10% in CEFR
prediction accuracy.
