A historical perspective of speech recognition

What do we know now that we did not know 40 years ago?

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[27]  Li Deng,et al.  Challenges in adopting speech recognition , 2004, CACM.

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[30]  Geoffrey E. Hinton,et al.  Binary coding of speech spectrograms using a deep auto-encoder , 2010, INTERSPEECH.

[31]  Lukás Burget,et al.  Extensions of recurrent neural network language model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[32]  Marc'Aurelio Ranzato,et al.  Large Scale Distributed Deep Networks , 2012, NIPS.

[33]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[34]  Tara N. Sainath,et al.  Scalable Minimum Bayes Risk Training of Deep Neural Network Acoustic Models Using Distributed Hessian-free Optimization , 2012, INTERSPEECH.

[35]  Dong Yu,et al.  Pipelined Back-Propagation for Context-Dependent Deep Neural Networks , 2012, INTERSPEECH.

[36]  Yifan Gong,et al.  Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[37]  Zhi-Jie Yan,et al.  A scalable approach to using DNN-derived features in GMM-HMM based acoustic modeling for LVCSR , 2013, INTERSPEECH.

[38]  Geoffrey Zweig,et al.  Recurrent neural networks for language understanding , 2013, INTERSPEECH.

[39]  Jinyu Li,et al.  Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks. , 2013, ICLR 2013.

[40]  M. Wester,et al.  Proceedings of ICASSP , 2014 .