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36 lines
1.6 KiB
Markdown
36 lines
1.6 KiB
Markdown
# MGB2
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The Multi-Dialect Broadcast News Arabic Speech Recognition (MGB-2):
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The second edition of the Multi-Genre Broadcast (MGB-2) Challenge is
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an evaluation of speech recognition and lightly supervised alignment
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using TV recordings in Arabic. The speech data is broad and multi-genre,
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spanning the whole range of TV output, and represents a challenging task for
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speech technology. In 2016, the challenge featured two new Arabic tracks based
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on TV data from Aljazeera. It was an official challenge at the 2016 IEEE
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Workshop on Spoken Language Technology. The 1,200 hours MGB-2: from Aljazeera
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TV programs have been manually captioned with no timing information.
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QCRI Arabic ASR system has been used to recognize all programs. The ASR output
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was used to align the manual captioning and produce speech segments for
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training speech recognition. More than 20 hours from 2015 programs have been
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transcribed verbatim and manually segmented. This data is split into a
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development set of 10 hours, and a similar evaluation set of 10 hours.
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Both the development and evaluation data have been released in the 2016 MGB
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challenge
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Official reference:
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Ali, Ahmed, et al. "The MGB-2 challenge: Arabic multi-dialect broadcast media recognition."
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2016 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2016.
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IEEE link: https://ieeexplore.ieee.org/abstract/document/7846277
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## Performance Record (after 3 epochs)
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| Decoding method | dev WER | test WER |
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|---------------------------|------------|---------|
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| attention-decoder | 27.87 | 26.12 |
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| whole-lattice-rescoring | 25.32 | 23.53 |
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See [RESULTS](/egs/mgb2/ASR/RESULTS.md) for details.
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