Aalto system for the 2017 Arabic multi-genre broadcast challenge

@article{Smit2017AaltoSF,
  title={Aalto system for the 2017 Arabic multi-genre broadcast challenge},
  author={Peter Smit and Siva Reddy Gangireddy and Seppo Enarvi and Sami Virpioja and Mikko Kurimo},
  journal={2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
  year={2017},
  pages={338-345}
}
We describe the speech recognition systems we have created for MGB-3, the 3rd Multi Genre Broadcast challenge, which this year consisted of a task of building a system for transcribing Egyptian Dialect Arabic speech, using a big audio corpus of primarily Modern Standard Arabic speech and only a small amount (5 hours) of Egyptian adaptation data. Our system, which was a combination of different acoustic models, language models and lexical units, achieved a Multi-Reference Word Error Rate of 29… CONTINUE READING

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Key Quantitative Results

  • Our system, which was a combination of different acoustic models, language models and lexical units, achieved a Multi-Reference Word Error Rate of 29.25%, which was the lowest in the competition. Also on the old MGB-2 task, which was run again to indicate progress, we achieved the lowest error rate: 13.2%.

References

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Showing 1-10 of 37 references

Development of the MIT ASR system for the 2016 Arabic Multi-genre Broadcast Challenge

  • Tuka AlHanai, Wei-Ning Hsu, James Glass
  • SLT 2016 – IEEE Spoken Language Technology…
  • 2016
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