The MGB-2 Challenge: Arabic Multi-dialect Broadcast Media Recognition
2016 · Ahmed Ali, Peter Bell, James Glass, et al.
Abstract
This paper describes the Arabic Multi-Genre Broadcast (MGB-2) Challenge for SLT-2016. Unlike last year's English MGB Challenge, which focused on recognition of diverse TV genres, this year, the challenge has an emphasis on handling the diversity in dialect in Arabic speech. Audio data comes from 19 distinct programmes from the Aljazeera Arabic TV channel between March 2005 and December 2015. Programmes are split into three groups: conversations, interviews, and reports. A total of 1,200 hours have been released with lightly supervised transcriptions for the acoustic modelling. For language modelling, we made available over 110M words crawled from Aljazeera Arabic website Aljazeera.net for a 10 year duration 2000-2011. Two lexicons have been provided, one phoneme based and one grapheme based. Finally, two tasks were proposed for this year's challenge: standard speech transcription, and word alignment. This paper describes the task data and evaluation process used in the MGB challenge, a
Authors
(none)
Tags
Stats
Related papers
- MIT-QCRI Arabic Dialect Identification System For The 2017 Multi-genre Broadcast Challenge (2017)8.60
- UTD-CRSS Submission For MGB-3 Arabic Dialect Identification: Front-end And Back-end Advancements On Broadcast Speech (2017)4.52
- LSTM-TDNN With Convolutional Front-end For Dialect Identification In The 2019 Multi-genre Broadcast Challenge (2019)0.00
- Dialectal Coverage And Generalization In Arabic Speech Recognition (2024)4.52
- The Second Multi-channel Multi-party Meeting Transcription Challenge (m2met) 2.0): A Benchmark For Speaker-attributed ASR (2023)6.77
- Summary On The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand Challenge (2022)10.35
- A Deep Learning Approach For Similar Languages, Varieties And Dialects (2019)0.00
- Towards One Model To Rule All: Multilingual Strategy For Dialectal Code-switching Arabic ASR (2021)9.03