TY - GEN
T1 - The MGB-5 Challenge
T2 - 2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019
AU - Ali, Ahmed
AU - Shon, Suwon
AU - Samih, Younes
AU - Mubarak, Hamdy
AU - Abdelali, Ahmed
AU - Glass, James
AU - Renals, Steve
AU - Choukri, Khalid
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Speech recognition
KW - broadcast speech
KW - dialect identification
KW - multi-reference WER
KW - multigenre
KW - under-resource
UR - https://www.scopus.com/pages/publications/85081565176
U2 - 10.1109/ASRU46091.2019.9003960
DO - 10.1109/ASRU46091.2019.9003960
M3 - Conference contribution
AN - SCOPUS:85081565176
T3 - 2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings
SP - 1026
EP - 1033
BT - 2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2019 through 18 December 2019
ER -