Quality Estimation for Automatic Speech Recognition

@inproceedings{Negri2014QualityEF,
  title={Quality Estimation for Automatic Speech Recognition},
  author={Matteo Negri and Marco Turchi and Jos{\'e} Guilherme Camargo de Souza and Daniele Falavigna},
  booktitle={COLING},
  year={2014}
}
We address the problem of estimating the quality of Automatic Speech Recognition (ASR) output at utterance level, without recourse to manual reference transcriptions and when information about system’s confidence is not accessible. Given a source signal and its automatic transcription, we approach this problem as a regression task where the word error rate of the transcribed utterance has to be predicted. To this aim, we explore the contribution of different feature sets and the potential of… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 42 REFERENCES

Condence Estimation for Automatic Speech Recognition Hypotheses

Matthew Stephen Seigel.
  • University of Cambridge. PhD Thesis.
  • 2013

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