Disentangled Representations for Arabic Dialect Identification based on Supervised Clustering with Triplet Loss

In this paper, we propose a novel supervised clustering with triplet (SCT) loss that effectively learns disentangled representations for Arabic dialect identification (ADI). To improve the performance of ADI using latent representation-based approaches, we need to extract embeddings that include only dialect related information by dissociating all the irrelevant information such as gender, channel, and speaker. In consideration of the embedding-level distribution, our proposed SCT loss minimizes intra-class variations and maximizes inter-class variations. Specifically, it uses the centroid of each dialect as a triplet component, thereby avoiding the issue of choosing an undesirable triplet component due to random sampling. Experimental results on the ADI-17 dataset show that our proposed method significantly outperforms conventional state-of-the-art methods in terms of the identification accuracy.