2024/06/05 by Bicheng Yan, Yan, Bi-Cheng, Wei-Cheng Chao +11
Computer Science · Psychology · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Natural Language Processing Techniques #Phonetics and Phonology Research #Sound (cs.SD) #Speech Recognition and Synthesis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.02859
openalex publication_date 2024/06/05 · openalex created_date 2024/06/08 · openalex updated_date 2026/07/28
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.