Moroccan ASR and Fine-Grained ADI Challenge: MGB-5
The fifth edition of the Multi-Genre Broadcast (MGB-5) is an evaluation of speech recognition and 17-classes dialect identification using youtube recordings in dialectal Arabic.
The MGB-5 is using 16 hours of multi-genre data collected from different YouTube channels.
In 2019, the challenge features two new Arabic tracks based on youtube recordings. It was an official challenge at the 2017 IEEE Automatic Speech Recognition and Understanding Workshop.
Moroccan Arabic Automatic Speech Recognition
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.
You can find the MGB-5 ASR baseline system here.
Fine-grained Arabic Dialect Identification (ADI)
The task of ADI is dialect identification of speech from YouTube to one of the 17 dialects (ADI17). The previous studies on Arabic dialect identification using audio signal is limited to 5 dialect classes by lack of speech corpus. To present a fine-grained analysis of the Arabic dialect speech, we collected the Arabic dialect from YouTube.
For Train set, about 3,000 hours of Arabic dialect speech data from 17 countries on the Arabic world was collected from YouTube. Since we collected the speech by considering the YouTube channels in a specific country, certain that the dataset might have some labeling errors. For this reason, we have two sub-tracks for the ADI task, supervised learning track and unsupervised track. Thus, the label of the train set can be either used or not and it completely depends on the choice of participants.
For the Dev and Test set, about 280 hours of speech data was collected from YouTube. After automatic speaker linking and dialect labeling by human annotators, we selected 57 hours of speech dataset to use as Dev and Test set for performance evaluation. The test dataset was considered to have three sub-categories by the segment duration to represent short (under 5 sec), medium(between 5 sec and 20 sec), long duration (over 20 sec) of the dialectal speech.