  Accurately classifying accents and assessing accentedness in non-native
speakers are both challenging tasks due to the complexity and diversity of
accent and dialect variations. In this study, embeddings from advanced
pre-trained language identification (LID) and speaker identification (SID)
models are leveraged to improve the accuracy of accent classification and
non-native accentedness assessment. Findings demonstrate that employing
pre-trained LID and SID models effectively encodes accent/dialect information
in speech. Furthermore, the LID and SID encoded accent information complement
an end-to-end accent identification (AID) model trained from scratch. By
incorporating all three embeddings, the proposed multi-embedding AID system
achieves superior accuracy in accent identification. Next, we investigate
leveraging automatic speech recognition (ASR) and accent identification models
to explore accentedness estimation. The ASR model is an end-to-end
connectionist temporal classification (CTC) model trained exclusively with
en-US utterances. The ASR error rate and en-US output of the AID model are
leveraged as objective accentedness scores. Evaluation results demonstrate a
strong correlation between the scores estimated by the two models.
Additionally, a robust correlation between the objective accentedness scores
and subjective scores based on human perception is demonstrated, providing
evidence for the reliability and validity of utilizing AID-based and ASR-based
systems for accentedness assessment in non-native speech.
