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Accurately classifying accents and assessing accentedness in non-nativespeakers are both challenging tasks due to the complexity and diversity ofaccent and dialect variations. In this study, embeddings from advancedpre-trained language identification (LID) and speaker identification (SID)models are leveraged to improve the accuracy of accent classification andnon-native accentedness assessment. Findings demonstrate that employingpre-trained LID and SID models effectively encodes accent/dialect informationin speech. Furthermore, the LID and SID encoded accent information complementan end-to-end accent identification (AID) model trained from scratch. Byincorporating all three embeddings, the proposed multi-embedding AID systemachieves superior accuracy in accent identification. Next, we investigateleveraging automatic speech recognition (ASR) and accent identification modelsto explore accentedness estimation. The ASR model is an end-to-endconnectionist temporal classification (CTC) model trained exclusively withen-US utterances. The ASR error rate and en-US output of the AID model areleveraged as objective accentedness scores. Evaluation results demonstrate astrong correlation between the scores estimated by the two models.Additionally, a robust correlation between the objective accentedness scoresand subjective scores based on human perception is demonstrated, providingevidence for the reliability and validity of utilizing AID-based and ASR-basedsystems for accentedness assessment in non-native speech.

Advanced accent/dialect identification and accentedness assessment with multi-embedding models and automatic speech recognition