Speech Recognition Challenge in the Wild: Arabic MGB-3
Table 1: MGB3 data distribution across the three classes, duration in hours/number of programs (12 minutes each). * is duration in hours across all speech segments
['[EMPTY]', 'Adapt', 'Dev', 'Test']
To build the MGB-3 corpus YouTube clips from various Egyptian channels were selected. Across the seven different genres a total of 80 videos were selected. From each video the first 12 minutes were selected. Manually-identified non-speech segments were removed. The resulting clips were then distributed into adaptation, development, and testing groups, with the test set being a little larger then the other two sets. The table also summarizes how much of the overall data contains overlapping speech (more than one speaker talking simultaneously) and how much data contains non-overlapping speech. The ASR baseline system was trained using the full MGB-2 data, 1,200 hours of audio. The lexicon was grapheme-based, covering 950 000 words collected from a set of shared lexicons, as well as the training data text. We report results for the MGB-2 development set (5002 non-overlapping speech segments) on which we achieve a WER of 22.6% without LM rescoring. This is a strong MGB-2 baseline compared to last year's results.