The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Understanding how Deep Belief Networks perform acoustic modelling
Abstract
Deep Belief Networks (DBNs) are a very competitive alternative to Gaussian mixture models for relating states of a hidden Markov model to frames of coefficients derived from the acoustic input. They are competitive for three reasons: DBNs can be fine-tuned as neural networks; DBNs have many non-linear hidden layers; and DBNs are generatively pre-trained. This paper illustrates how each of these three aspects contributes to the DBN's good recognition performance using both phone recognition performance on the TIMIT corpus and a dimensionally reduced visualization of the relationships between the feature vectors learned by the DBNs that preserves the similarity structure of the feature vectors at multiple scales. The same two methods are also used to investigate the most suitable type of input representation for a DBN.
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Cites
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Self-citationConnectionist Speech Recognition: A Hybrid Approach
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Making Deep Belief Networks effective for large vocabulary continuous speech recognition
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Cited by
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-citationDeep Learning: Methods and Applications
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Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,759
Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks
Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178838 · Cited by 1,678
Deep Learning for Audio Signal Processing
Purwins, H., Li, B., Virtanen, T., Schluter, J., Chang, S.-Y., & Sainath, T. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2908700 · Cited by 855
Metadata
- Authors
- Mohamed, A.-R., Hinton, G., & Penn, G.
- Venue
- 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2012-03
- Pages
- 4273-4276
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2172097686
- Cited by
- 265
- References
- 10
- FWCI
- 21.67
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Mohamed, A.-R., Hinton, G., & Penn, G. (2012). Understanding how Deep Belief Networks perform acoustic modelling. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2012.6288863
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