The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Adaptation of context-dependent deep neural networks for automatic speech recognition
Abstract
In this paper, we evaluate the effectiveness of adaptation methods for context-dependent deep-neural-network hidden Markov models (CD-DNN-HMMs) for automatic speech recognition. We investigate the affine transformation and several of its variants for adapting the top hidden layer. We compare the affine transformations against direct adaptation of the softmax layer weights. The feature-space discriminative linear regression (fDLR) method with the affine transformations on the input layer is also evaluated. On a large vocabulary speech recognition task, a stochastic gradient ascent implementation of the fDLR and the top hidden layer adaptation is shown to reduce word error rates (WERs) by 17% and 14%, respectively, compared to the baseline DNN performances. With a batch update implementation, the softmax layer adaptation technique reduces WERs by 10%. We observe that using bias shift performs as well as doing scaling plus bias shift.
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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
Self-citationTraining Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 2×
“(2) The DNN may be initialized using " pre-training " , which uses a contrast divergence algorithm on restricted Boltzmann machine RBM [14] for layers up to L − 1.”
MethodContext-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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Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
Self-citationDeep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,759
Self-citationSpeech Recognition Using Deep Neural Networks: A Systematic Review
Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019) · IEEE Access · doi:10.1109/access.2019.2896880 · Cited by 1,152
Recent advances in deep learning for speech research at Microsoft
Deng, L., Li, J., Huang, J.-T., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., Williams, J., et al. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639345 · Cited by 801
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Metadata
- Authors
- Yao, K., Yu, D., Seide, F., Su, H., Deng, L., & Gong, Y.
- Venue
- 2012 IEEE Spoken Language Technology Workshop (SLT)
- Publisher
- IEEE
- Published
- 2012-12
- Pages
- 366-369
- OpenAlex
- W2087006792
- Cited by
- 194
- References
- 3
- FWCI
- 15.26
- Type
- conference paper
- Language
- en
- Open access
- closed
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