  As an indispensable ingredient of computer-assisted pronunciation training
(CAPT), automatic pronunciation assessment (APA) plays a pivotal role in aiding
self-directed language learners by providing multi-aspect and timely feedback.
However, there are at least two potential obstacles that might hinder its
performance for practical use. On one hand, most of the studies focus
exclusively on leveraging segmental (phonetic)-level features such as goodness
of pronunciation (GOP); this, however, may cause a discrepancy of feature
granularity when performing suprasegmental (prosodic)-level pronunciation
assessment. On the other hand, automatic pronunciation assessments still suffer
from the lack of large-scale labeled speech data of non-native speakers, which
inevitably limits the performance of pronunciation assessment. In this paper,
we tackle these problems by integrating multiple prosodic and phonological
features to provide a multi-view, multi-granularity, and multi-aspect (3M)
pronunciation modeling. Specifically, we augment GOP with prosodic and
self-supervised learning (SSL) features, and meanwhile develop a
vowel/consonant positional embedding for a more phonology-aware automatic
pronunciation assessment. A series of experiments conducted on the
publicly-available speechocean762 dataset show that our approach can obtain
significant improvements on several assessment granularities in comparison with
previous work, especially on the assessment of speaking fluency and speech
prosody.
