The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
New types of deep neural network learning for speech recognition and related applications: an overview
Abstract
In this paper, we provide an overview of the invited and contributed papers presented at the special session at ICASSP-2013, entitled “New Types of Deep Neural Network Learning for Speech Recognition and Related Applications,” as organized by the authors. We also describe the historical context in which acoustic models based on deep neural networks have been developed. The technical overview of the papers presented in our special session is organized into five ways of improving deep learning methods: (1) better optimization; (2) better types of neural activation function and better network architectures; (3) better ways to determine the myriad hyper-parameters of deep neural networks; (4) more appropriate ways to preprocess speech for deep neural networks; and (5) ways of leveraging multiple languages or dialects that are more easily achieved with deep neural networks than with Gaussian mixture models.
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Cites
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Self-citationGradient-based learning applied to document recognition
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Self-citationSpeech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Self-citationPractical Bayesian Optimization of Machine Learning Algorithms
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Self-citationRecurrent neural network based language model
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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
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Cited by
Playing Atari with Deep Reinforcement Learning
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SGDR: Stochastic Gradient Descent with Warm Restarts
Loshchilov, I., & Hutter, F. (2016) · arXiv · doi:10.48550/arxiv.1608.03983 · Cited by 5,309
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
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Self-citationConvolutional, Long Short-Term Memory, fully connected Deep Neural Networks
Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178838 · Cited by 1,678
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719367115 · Cited by 1,230
Metadata
- Authors
- Deng, L., Hinton, G., & Kingsbury, B.
- Venue
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 8599-8603
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2091432990
- Cited by
- 1,178
- References
- 12
- FWCI
- 32.74
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Deng, L., Hinton, G., & Kingsbury, B. (2013). New types of deep neural network learning for speech recognition and related applications: an overview. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.1109/icassp.2013.6639344
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