The Multi-Dialect Broadcast News Arabic Speech Recognition (MGB-2): The second edition of the Multi-Genre Broadcast (MGB-2) Challenge is an evaluation of speech recognition and lightly supervised alignment using TV recordings in Arabic. The speech data is broad and multi-genre, spanning the whole range of TV output, and represents a challenging task for speech technology. In 2016, the challenge featured two new Arabic tracks based on TV data from Aljazeera. It was an official challenge at the 2016 IEEE Workshop on Spoken Language Technology. The 1,200 hours MGB-2: from Aljazeera TV programs have been manually captioned with no timing information. QCRI Arabic ASR system has been used to recognize all programs. The ASR output was used to align the manual captioning and produce speech segments for training speech recognition. More than 20 hours from 2015 programs have been transcribed verbatim and manually segmented. This data is split into a development set of 10 hours, and a similar evaluation set of 10 hours. Both the development and evaluation data have been released in the 2016 MGB challenge
Official reference:
Ali, Ahmed, et al. "The MGB-2 challenge: Arabic multi-dialect broadcast media recognition." 2016 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2016.
IEEE link: https://ieeexplore.ieee.org/abstract/document/7846277
| dev | test | comment | |
|---|---|---|---|
| greedy search | 15.52 | 15.28 | --epoch 18, --avg 5, --max-duration 200 |
| modified beam search | 13.88 | 13.7 | --epoch 18, --avg 5, --max-duration 200 |
| fast beam search | 14.62 | 14.36 | --epoch 18, --avg 5, --max-duration 200 |
| Decoding method | dev WER | test WER |
|---|---|---|
| attention-decoder | 15.62 | 15.01 |
| whole-lattice-rescoring | 15.89 | 15.08 |
See RESULTS for details.