  The disparity in phonology between learner's native (L1) and target (L2)
language poses a significant challenge for mispronunciation detection and
diagnosis (MDD) systems. This challenge is further intensified by lack of
annotated L2 data. This paper proposes a novel MDD architecture that exploits
multiple `views' of the same input data assisted by auxiliary tasks to learn
more distinctive phonetic representation in a low-resource setting. Using the
mono- and multilingual encoders, the model learn multiple views of the input,
and capture the sound properties across diverse languages and accents. These
encoded representations are further enriched by learning articulatory features
in a multi-task setup. Our reported results using the L2-ARCTIC data
outperformed the SOTA models, with a phoneme error rate reduction of 11.13% and
8.60% and absolute F1 score increase of 5.89%, and 2.49% compared to the
single-view mono- and multilingual systems, with a limited L2 dataset.
