ADI17: A Fine-Grained Arabic Dialect Identification Dataset
By Suwon Shon, Ahmed M. Ali, Younes Samih, Hamdy Mubarak, James R. Glass. 2020
51 citations recorded by the source corpus.
In this paper, we describe a method to collect dialectal speech from YouTube videos to create a large-scale Dialect Identification (DID) dataset. Using this method, we collected dialectal Arabic from known YouTube channels from 17 Arabic speaking countries in the Middle East and Northern Africa. After a refinement process, a total of 3,000 hours of speech was available for training DID systems, with an additional 57 hours of speech for development and testing. For detailed evaluations, the DID data was divided into three sub-categories based on the segment duration: short (less than 5s), medium (5-20s), and long (over 20s). We compare state-of-the-art DID techniques on these data, and also analyze a DID system trained on these data. Since the training and test data share the same channel domain, we also used the Multi-Genre Broadcast 3 (MGB-3) test set to evaluate on domain mismatched condition.
Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks, Computational Linguistics & Semantics, Signal Processing
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Citations in the corpus
- VOXLINGUA107: A Dataset for Spoken Language Recognition (2020, methodology): “Another dataset that uses YouTube audio is ADI17 [4], built for the MGB-5 Challenge [5] Arabic dialect identification task.”