Achieving Human Parity in Conversational Speech Recognition

@article{Xiong2016AchievingHP,
  title={Achieving Human Parity in Conversational Speech Recognition},
  author={Wayne Xiong and Jasha Droppo and Xuedong Huang and Frank Seide and Mike Seltzer and Andreas Stolcke and Dong Yu and Geoffrey Zweig},
  journal={CoRR},
  year={2016},
  volume={abs/1610.05256}
}
Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s. In this paper, we measure the human error rate on the widely used NIST 2000 test set, and find that our latest automated system has reached human parity. The error rate of professional transcribers is 5.9% for the Switchboard portion of the data, in which newly acquainted pairs of people discuss an assigned topic, and 11.3% for the CallHome portion where… CONTINUE READING
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  • The error rate of professional transcribers is 5.9% for the Switchboard portion of the data, in which newly acquainted pairs of people discuss an assigned topic, and 11.3% for the CallHome portion where friends and family members have open-ended conversations. In both cases, our automated system establishes a new state of the art, and edges past the human benchmark, achieving error rates of 5.8% and 11.0%, respectively.
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