MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge
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.
Keywords
Cite
@article{arxiv.1709.00387,
title = {MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge},
author = {Suwon Shon and Ahmed Ali and James Glass},
journal= {arXiv preprint arXiv:1709.00387},
year = {2017}
}
Comments
Submitted to the 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2017)