Phone recognition with hierarchical convolutional deep maxout networks

@article{Tth2015PhoneRW,
  title={Phone recognition with hierarchical convolutional deep maxout networks},
  author={L. T{\'o}th},
  journal={EURASIP Journal on Audio, Speech, and Music Processing},
  year={2015},
  volume={2015},
  pages={1-13}
}
  • L. Tóth
  • Published 2015
  • Computer Science
  • EURASIP Journal on Audio, Speech, and Music Processing
  • Deep convolutional neural networks (CNNs) have recently been shown to outperform fully connected deep neural networks (DNNs) both on low-resource and on large-scale speech tasks. Experiments indicate that convolutional networks can attain a 10–15 % relative improvement in the word error rate of large vocabulary recognition tasks over fully connected deep networks. Here, we explore some refinements to CNNs that have not been pursued by other authors. First, the CNN papers published up till now… CONTINUE READING
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    References

    SHOWING 1-10 OF 69 REFERENCES
    Deep Convolutional Neural Networks for Large-scale Speech Tasks
    • 323
    Deep convolutional neural networks for LVCSR
    • 816
    • PDF
    Improvements to Deep Convolutional Neural Networks for LVCSR
    • 168
    • PDF
    An analysis of convolutional neural networks for speech recognition
    • Jui-Ting Huang, J. Li, Y. Gong
    • Computer Science
    • 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
    • 2015
    • 79
    • PDF
    Convolutional deep rectifier neural nets for phone recognition
    • 31
    • PDF
    Deep maxout neural networks for speech recognition
    • 57
    Improving deep neural networks for LVCSR using rectified linear units and dropout
    • 957
    • PDF
    Convolutional maxout neural networks for low-resource speech recognition
    • 14
    Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition
    • 700
    • PDF