The MGB-5 Arabic data comprises 14 hours of Moroccan Arabic speech extracted from 93 YouTube videos distributed across seven genres: comedy, cooking, family/children, fashion, drama, sports, and science clips. We assume that the MGB-5 data is not enough by itself to build robust speech recognition systems, but could be useful for adaptation, and for hyper-parameter tuning of models built using the MGB-2 data. Therefore, we suggest to reuse the MGB-2 training data in this challenge, and consider the provided in-domain data as (supervised) adaptation data. Given that dialectal Arabic does not have a clearly defined orthography, different people tend to write the same word in slightly different forms. Therefore, instead of developing strict guidelines to ensure a standardized orthography, variations in spelling are allowed. Thus multiple transcriptions were produced, allowing transcribers to write the transcripts as they deemed correct. Every file has been segmented and transcribed by four different Moroccan annotators.

The 93 YouTube clips have been manually labeled for speech, non-speech segments. About 12 minutes from each program were selected for transcription. The resulting speech segments were then distributed into train, development, and test data sets as follows:

  • Training data: 10.2 hours from 69 programs
  • Development data: 1.8 hours from 10 programs
  • Testing data: 2.0 hours from 14 programs

In addition to the transcribed 14 hours, the full programs are also provided, which amounts to 48 hours for the 93 programs. This data can be used for in-domain speech or genre adaptation. You can find samples here: audio, segmentation, transcription in Arabic, and transcription in Buckwalter.