The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription
Abstract
We investigate the potential of Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, from a feature-engineering perspective. Recently, we had shown that for speaker-independent transcription of phone calls (NIST RT03S Fisher data), CD-DNN-HMMs reduced the word error rate by as much as one third-from 27.4%, obtained by discriminatively trained Gaussian-mixture HMMs with HLDA features, to 18.5%-using 300+ hours of training data (Switchboard), 9000+ tied triphone states, and up to 9 hidden network layers.
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Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
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
Greedy 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
Self-citationA Practical Guide to Training Restricted Boltzmann Machines
Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852
Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms
White, B. W., & Rosenblatt, F. (1963) · The American Journal of Psychology · doi:10.2307/1419730 · Cited by 1,792
PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS
ROSENBLATT, F. (1961) · doi:10.21236/ad0256582 · Cited by 1,691
Maximum likelihood linear transformations for HMM-based speech recognition
Gales, M. J. F. (1998) · Computer Speech & Language · doi:10.1006/csla.1998.0043 · Cited by 1,482
Conversational speech transcription using context-dependent deep neural networks
Seide, F., Li, G., & Yu, D. (2011) · Interspeech 2011 · doi:10.21437/interspeech.2011-169 · Cited by 852
Self-citationMean Field Theory for Sigmoid Belief Networks
Saul, L. K., Jaakkola, T., & Jordan, M. I. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.251 · Cited by 327
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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
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A time delay neural network architecture for efficient modeling of long temporal contexts
Peddinti, V., Povey, D., & Khudanpur, S. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-647 · Cited by 986
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
Metadata
- Authors
- Seide, F., Li, G., Chen, X., & Yu, D.
- Venue
- 2011 IEEE Workshop on Automatic Speech Recognition & Understanding
- Publisher
- IEEE
- Published
- 2011-12
- Pages
- 24-29
- OpenAlex
- W2160306971
- Cited by
- 632
- References
- 19
- FWCI
- 44.97
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Seide, F., Li, G., Chen, X., & Yu, D. (2011). Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription. 2011 IEEE Workshop on Automatic Speech Recognition & Understanding. https://doi.org/10.1109/asru.2011.6163899
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