  Automatic pronunciation assessment (APA) manages to evaluate the
pronunciation proficiency of a second language (L2) learner in a target
language. Existing efforts typically draw on regression models for proficiency
score prediction, where the models are trained to estimate target values
without explicitly accounting for phoneme-awareness in the feature space. In
this paper, we propose a contrastive phonemic ordinal regularizer (ConPCO)
tailored for regression-based APA models to generate more
phoneme-discriminative features while considering the ordinal relationships
among the regression targets. The proposed ConPCO first aligns the phoneme
representations of an APA model and textual embeddings of phonetic
transcriptions via contrastive learning. Afterward, the phoneme characteristics
are retained by regulating the distances between inter- and intra-phoneme
categories in the feature space while allowing for the ordinal relationships
among the output targets. We further design and develop a hierarchical APA
model to evaluate the effectiveness of our method. Extensive experiments
conducted on the speechocean762 benchmark dataset suggest the feasibility and
efficacy of our approach in relation to some cutting-edge baselines.
