Title: UTD-CRSS Submission for MGB-3 Arabic Dialect Identification: Front-end and Back-end Advancements on Broadcast Speech,
Abstract: This study presents systems submitted by the University of Texas at Dallas,
Center for Robust Speech Systems (UTD-CRSS) to the MGB-3 Arabic Dialect
Identification (ADI) subtask. This task is defined to discriminate between five
dialects of Arabic, including Egyptian, Gulf, Levantine, North African, and
Modern Standard Arabic. We develop multiple single systems with different
front-end representations and back-end classifiers. At the front-end level,
feature extraction methods such as Mel-frequency cepstral coefficients (MFCCs)
and two types of bottleneck features (BNF) are studied for an i-Vector
framework. As for the back-end level, Gaussian back-end (GB), and Generative
Adversarial Networks (GANs) classifiers are applied alternately. The best
submission (contrastive) is achieved for the ADI subtask with an accuracy of
76.94% by augmenting the randomly chosen part of the development dataset.
Further, with a post evaluation correction in the submitted system, final
accuracy is increased to 79.76%, which represents the best performance achieved
so far for the challenge on the test dataset.