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This paper presents three innovative deep learning models for English accentclassification: Multi-DenseNet, PSA-DenseNet, and MPSE-DenseNet, that combinemulti-task learning and the PSA module attention mechanism with DenseNet. Weapplied these models to data collected from six dialects of English acrossnative English speaking regions (Britain, the United States, Scotland) andnonnative English speaking regions (China, Germany, India). Our experimentalresults show a significant improvement in classification accuracy, particularlywith MPSA-DenseNet, which outperforms all other models, including DenseNet andEPSA models previously used for accent identification. Our findings indicatethat MPSA-DenseNet is a highly promising model for accurately identifyingEnglish accents.

MPSA-DenseNet: A novel deep learning model for English accent classification