MPSA-DenseNet: A novel deep learning model for English accent
  classification
Tianyu Song Linh Thi Hoai Nguyen Ton Viet Ta
This paper presents three innovative deep learning models for English accent
classification: Multi-DenseNet, PSA-DenseNet, and MPSE-DenseNet, that combine
multi-task learning and the PSA module attention mechanism with DenseNet. We
applied these models to data collected from six dialects of English across
native English speaking regions (Britain, the United States, Scotland) and
nonnative English speaking regions (China, Germany, India). Our experimental
results show a significant improvement in classification accuracy, particularly
with MPSA-DenseNet, which outperforms all other models, including DenseNet and
EPSA models previously used for accent identification. Our findings indicate
that MPSA-DenseNet is a highly promising model for accurately identifying
English accents.