  Accent recognition with deep learning framework is a similar work to deep
speaker identification, they're both expected to give the input speech an
identifiable representation.
  Compared with the individual-level features learned by speaker identification
network, the deep accent recognition work throws a more challenging point that
forging group-level accent features for speakers.
  In this paper, we borrow and improve the deep speaker identification
framework to recognize accents, in detail, we adopt Convolutional Recurrent
Neural Network as front-end encoder and integrate local features using
Recurrent Neural Network to make an utterance-level accent representation.
  Novelly, to address overfitting, we simply add Connectionist Temporal
Classification based speech recognition auxiliary task during training, and for
ambiguous accent discrimination, we introduce some powerful discriminative loss
functions in face recognition works to enhance the discriminative power of
accent features.
  We show that our proposed network with discriminative training method
(without data-augment) is significantly ahead of the baseline system on the
accent classification track in the Accented English Speech Recognition
Challenge 2020, where the loss function Circle-Loss has achieved the best
discriminative optimization for accent representation.
