Semi-supervised Learning with Generative Adversarial Networks for Arabic Dialect Identification

Dialect Identification (DID) refers to the process of identifying different dialects within the same language class. Compared with more general language identification (LID), DID is a more challenging task because of the substantial similarity between dialects. For an i-vector based LID/DID, prior studies have shown advancements with deep neural networks (DNNs) over Gaussian Mixture Models (GMMs) in acoustic modeling. In this study, a novel i-vector representation which is based on unsupervised bottleneck features is examined as the feature to identify dialects from Arabic broadcast speech. To utilize the unlabeled training data, semi-supervised learning with generative adversarial networks (GANs) are incorporated in the back-end classifier development. Experiments with the proposed method in the third release version of the Multi-Genre Broadcast (MGB-3) Challenge yields the best single system performance among all submitted systems. An overall classification accuracy of 73.8% achieves a +28.8% relative improvement over the MGB-3 baseline with an accuracy of 57.3%, which is the state-of-the-art performance in this DID task. The fused system further achieves an improvement of +39.4% in accuracy.