The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Making Deep Belief Networks effective for large vocabulary continuous speech recognition
Abstract
To date, there has been limited work in applying Deep Belief Networks (DBNs) for acoustic modeling in LVCSR tasks, with past work using standard speech features. However, a typical LVCSR system makes use of both feature and model-space speaker adaptation and discriminative training. This paper explores the performance of DBNs in a state-of-the-art LVCSR system, showing improvements over Multi-Layer Perceptrons (MLPs) and GMM/HMMs across a variety of features on an English Broadcast News task. In addition, we provide a recipe for data parallelization of DBN training, showing that data parallelization can provide linear speed-up in the number of machines, without impacting WER.
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Cites
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
Cited by
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,759
Improving deep neural networks for LVCSR using rectified linear units and dropout
Dahl, G. E., Sainath, T. N., & Hinton, G. E. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639346 · Cited by 1,310
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
Metadata
- Authors
- Sainath, T. N., Kingsbury, B., Ramabhadran, B., Fousek, P., Novak, P., & Mohamed, A.-R.
- Venue
- 2011 IEEE Workshop on Automatic Speech Recognition & Understanding
- Publisher
- IEEE
- Published
- 2011-12
- Pages
- 30-35
- OpenAlex
- W2076794394
- Cited by
- 189
- References
- 1
- FWCI
- 22.36
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Sainath, T. N., Kingsbury, B., Ramabhadran, B., Fousek, P., Novak, P., & Mohamed, A.-R. (2011). Making Deep Belief Networks effective for large vocabulary continuous speech recognition. 2011 IEEE Workshop on Automatic Speech Recognition & Understanding. https://doi.org/10.1109/asru.2011.6163900
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