The MGB-5 Challenge: Recognition and Dialect Identification of Dialectal Arabic Speech

Ahmed Ali, Suwon Shon, Younes Samih, Hamdy Mubarak, Ahmed Abdelali, James Glass, Steve Renals, Khalid Choukri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes the fifth edition of the Multi-Genre Broadcast Challenge (MGB-5), an evaluation focused on Arabic speech recognition and dialect identification. MGB-5 extends the previous MGB-3 challenge in two ways: first it focuses on Moroccan Arabic speech recognition; second the granularity of the Arabic dialect identification task is increased from 5 dialect classes to 17, by collecting data from 17 Arabic speaking countries. Both tasks use YouTube recordings to provide a multi-genre multi-dialectal challenge in the wild. Moroccan speech transcription used about 13 hours of transcribed speech data, split across training, development, and test sets, covering 7-genres: comedy, cooking, family/kids, fashion, drama, sports, and science (TEDx). The fine-grained Arabic dialect identification data was collected from known YouTube channels from 17 Arabic countries. 3,000 hours of this data was released for training, and 57 hours for development and testing. The dialect identification data was divided into three sub-categories based on the segment duration: short (under 5 s), medium (5-20 s), and long (>20 s). Overall, 25 teams registered for the challenge, and 9 teams submitted systems for the two tasks. We outline the approaches adopted in each system and summarize the evaluation results.

Original languageEnglish
Title of host publication2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1026-1033
Number of pages8
ISBN (Electronic)9781728103068
DOIs
Publication statusPublished - Dec 2019
Event2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Singapore, Singapore
Duration: 15 Dec 201918 Dec 2019

Publication series

Name2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings

Conference

Conference2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019
CountrySingapore
CitySingapore
Period15/12/1918/12/19

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Keywords

  • Speech recognition
  • broadcast speech
  • dialect identification
  • multi-reference WER
  • multigenre
  • under-resource

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Linguistics and Language
  • Communication

Cite this

Ali, A., Shon, S., Samih, Y., Mubarak, H., Abdelali, A., Glass, J., Renals, S., & Choukri, K. (2019). The MGB-5 Challenge: Recognition and Dialect Identification of Dialectal Arabic Speech. In 2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings (pp. 1026-1033). [9003960] (2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASRU46091.2019.9003960