Qiang Fu

Affiliations:
  • Fetch Technologies, El Segundo, CA, USA
  • Georgia Institute of Technology, Atlanta, GA, USA (PhD 2008)


According to our database1, Qiang Fu authored at least 11 papers between 2005 and 2012.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2012
Automatic Speech Recognition Based on Non-Uniform Error Criteria.
IEEE Trans. Speech Audio Process., 2012

2010
Empirical system learning for statistical pattern recognition with non-uniform error criteria.
IEEE Trans. Signal Process., 2010

2008
A generalization of the minimum classification error (MCE) training method for speech recognition and detection.
PhD thesis, 2008

Utilizing non-uniform cost learning for active control of inter-class confusion.
Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008

An Investigation of Non-Uniform Error Cost Function Design in Automatic Speech Recognition.
Proceedings of the Seventh International Conference on Machine Learning and Applications, 2008

Non-Uniform error criteria for automatic pattern and speech recognition.
Proceedings of the IEEE International Conference on Acoustics, 2008

2007
A study on rescoring using HMM-based detectors for continuous speech recognition.
Proceedings of the IEEE Workshop on Automatic Speech Recognition & Understanding, 2007

Automatic speech recognition based on weighted minimum classification error (W-MCE) training method.
Proceedings of the IEEE Workshop on Automatic Speech Recognition & Understanding, 2007

2006
Generalization of the minimum classification error (MCE) training based on maximizing generalized posterior probability (GPP).
Proceedings of the Ninth International Conference on Spoken Language Processing, 2006

Investigation on rescoring using minimum verification error (MVE) detectors.
Proceedings of the Ninth International Conference on Spoken Language Processing, 2006

2005
Segment-based phonetic class detection using minimum verification error (MVE) training.
Proceedings of the 9th European Conference on Speech Communication and Technology, 2005


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