The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Acoustic Modeling Using Deep Belief Networks
Abstract
Gaussian mixture models are currently the dominant technique for modeling the emission distribution of hidden Markov models for speech recognition. We show that better phone recognition on the TIMIT dataset can be achieved by replacing Gaussian mixture models by deep neural networks that contain many layers of features and a very large number of parameters. These networks are first pre-trained as a multi-layer generative model of a window of spectral feature vectors without making use of any discriminative information. Once the generative pre-training has designed the features, we perform discriminative fine-tuning using backpropagation to adjust the features slightly to make them better at predicting a probability distribution over the states of monophone hidden Markov models.
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Cites
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Self-citationTraining Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 4×
“gradient ascent in the log likelihood quite well by just addi ng the derivatives for the first two layers [10].”
BackgroundMethodInfluentialSelf-citationGreedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011
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Cited by
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Self-citation(2014) · Cited by 33,419
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
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
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Metadata
- Authors
- Mohamed, A.-R., Dahl, G. E., & Hinton, G.
- Venue
- IEEE Transactions on Audio, Speech, and Language Processing
- Published
- 2012-01
- Volume
- 20
- Issue
- 1
- Pages
- 14-22
- ISSN
- 1558-7916 (ISSN-L)
- 1558-7924
- OpenAlex
- W1993882792
- Cited by
- 1,727
- References
- 4
- FWCI
- 104.96
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mohamed, A.-R., Dahl, G. E., & Hinton, G. (2012). Acoustic Modeling Using Deep Belief Networks. IEEE Transactions on Audio, Speech, and Language Processing. https://doi.org/10.1109/tasl.2011.2109382
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