The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition
Abstract
We propose a novel regularized adaptation technique for context dependent deep neural network hidden Markov models (CD-DNN-HMMs). The CD-DNN-HMM has a large output layer and many large hidden layers, each with thousands of neurons. The huge number of parameters in the CD-DNN-HMM makes adaptation a challenging task, esp. when the adaptation set is small. The technique developed in this paper adapts the model conservatively by forcing the senone distribution estimated from the adapted model to be close to that from the unadapted model. This constraint is realized by adding Kullback-Leibler divergence (KLD) regularization to the adaptation criterion. We show that applying this regularization is equivalent to changing the target distribution in the conventional backpropagation algorithm. Experiments on Xbox voice search, short message dictation, and Switchboard and lecture speech transcription tasks demonstrate that the proposed adaptation technique can provide 2%-30% relative error reduction against the already very strong speaker independent CD-DNN-HMM systems using different adaptation sets under both supervised and unsupervised adaptation setups.
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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-citationPractical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
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-citationDeep Neural Networks for Acoustic Modeling in Speech Recognition
Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al. (2012) · Cited by 1,839
Self-citationAcoustic Modeling Using Deep Belief Networks
Mohamed, A.-R., Dahl, G. E., & Hinton, G. (2012) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2011.2109382 · Cited by 1,727
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-citationAn investigation of deep neural networks for noise robust speech recognition
Seltzer, M. L., Yu, D., & Wang, Y. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639100 · Cited by 640
Self-citationSeide, F., Li, G., Chen, X., & Yu, D. (2011) · 2011 IEEE Workshop on Automatic Speech Recognition & Understanding · doi:10.1109/asru.2011.6163899 · Cited by 632
Self-citationApplication of pretrained deep neural networks to large vocabulary speech recognition
Jaitly, N., Nguyen, P., Senior, A., & Vanhoucke, V. (2012) · Interspeech 2012 · doi:10.21437/interspeech.2012-10 · Cited by 243
Kingsbury, B., Sainath, T. N., & Soltau, H. (2012) · Interspeech 2012 · doi:10.21437/interspeech.2012-3 · Cited by 220
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Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
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Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,759
Deep learning for smart manufacturing: Methods and applications
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.003 · Cited by 1,740
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
Self-citationSwietojanski, P., & Renals, S. (2014) · 2014 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2014.7078569 · Cited by 218
Metadata
- Authors
- Yu, D., Yao, K., Su, H., Li, G., & Seide, F.
- Venue
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 7893-7897
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1989549063
- Cited by
- 408
- References
- 25
- FWCI
- 31.87
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yu, D., Yao, K., Su, H., Li, G., & Seide, F. (2013). KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.1109/icassp.2013.6639201
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