MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge

TL;DR

This paper presents a robust Arabic Dialect Identification system for the 2017 MGB-3 challenge, combining neural networks and i-vector techniques to distinguish dialects with high accuracy despite domain variability.

Contribution

It introduces a novel ADI system utilizing Siamese neural networks and i-vector post-processing to handle dialect variability and domain mismatches.

Findings

Achieved 75% accuracy on the test set

Effectively distinguished four dialects and Modern Standard Arabic

Demonstrated robustness against domain mismatches

Abstract

In order to successfully annotate the Arabic speech con- tent found in open-domain media broadcasts, it is essential to be able to process a diverse set of Arabic dialects. For the 2017 Multi-Genre Broadcast challenge (MGB-3) there were two possible tasks: Arabic speech recognition, and Arabic Dialect Identification (ADI). In this paper, we describe our efforts to create an ADI system for the MGB-3 challenge, with the goal of distinguishing amongst four major Arabic dialects, as well as Modern Standard Arabic. Our research fo- cused on dialect variability and domain mismatches between the training and test domain. In order to achieve a robust ADI system, we explored both Siamese neural network models to learn similarity and dissimilarities among Arabic dialects, as well as i-vector post-processing to adapt domain mismatches. Both Acoustic and linguistic features were used for the final MGB-3 submissions, with the best primary system achieving 75% accuracy on the official 10hr test set.