Corpus ID: 16489696

Rectifier Nonlinearities Improve Neural Network Acoustic Models

@inproceedings{Maas2013RectifierNI,
  title={Rectifier Nonlinearities Improve Neural Network Acoustic Models},
  author={Andrew L. Maas},
  year={2013}
}
Deep neural network acoustic models produce substantial gains in large vocabulary continuous speech recognition systems. Emerging work with rectified linear (ReL) hidden units demonstrates additional gains in final system performance relative to more commonly used sigmoidal nonlinearities. In this work, we explore the use of deep rectifier networks as acoustic models for the 300 hour Switchboard conversational speech recognition task. Using simple training procedures without pretraining… Expand
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  • L. Tóth
  • Computer Science
  • 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI)
  • 2018
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