Recent advances in ASR applied to an Arabic transcription system for Al-Jazeera

@inproceedings{Cardinal2014RecentAI,
  title={Recent advances in ASR applied to an Arabic transcription system for Al-Jazeera},
  author={Patrick Cardinal and Ahmed M. Ali and Najim Dehak and Yu Zhang and Tuka Al Hanai and Yifan Zhang and James R. Glass and Stephan Vogel},
  booktitle={INTERSPEECH},
  year={2014}
}
This paper describes a detailed comparison of several state-ofthe-art speech recognition techniques applied to a limited Arabic broadcast news dataset. The different approaches were all trained on 50 hours of transcribed audio from the Al-Jazeera news channel. The best results were obtained using i-vectorbased speaker adaptation in a training scenario using the Minimum Phone Error (MPE) criteria combined with sequential Deep Neural Network (DNN) training. We report results for two different… CONTINUE READING

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Key Quantitative Results

  • We report results for two different types of test data: broadcast news reports, with a best word error rate (WER) of 17.86%, and a broadcast conversations with a best WER of 29.85%.

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