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Deep Belief Networks using discriminative features for phone recognition
Abstract
Deep Belief Networks (DBNs) are multi-layer generative models. They can be trained to model windows of coefficients extracted from speech and they discover multiple layers of features that capture the higher-order statistical structure of the data. These features can be used to initialize the hidden units of a feed-forward neural network that is then trained to predict the HMM state for the central frame of the window. Initializing with features that are good at generating speech makes the neural network perform much better than initializing with random weights. DBNs have already been used successfully for phone recognition with input coefficients that are MFCCs or filterbank outputs. In this paper, we demonstrate that they work even better when their inputs are speaker adaptive, discriminative features. On the standard TIMIT corpus, they give phone error rates of 19.6% using monophone HMMs and a bigram language model and 19.4% using monophone HMMs and a trigram language model.
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Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1×
“〉model takes exponential time to compute exactly, but the efficient Contrastive Divergence (CD) approximation to the gradient can be used instead [11].”
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Metadata
- Authors
- Mohamed, A.-R., Sainath, T. N., Dahl, G., Ramabhadran, B., Hinton, G. E., & Picheny, M. A.
- Venue
- 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2011-05
- Pages
- 5060-5063
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2106051978
- Cited by
- 286
- References
- 2
- FWCI
- 17.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Mohamed, A.-R., Sainath, T. N., Dahl, G., Ramabhadran, B., Hinton, G. E., & Picheny, M. A. (2011). Deep Belief Networks using discriminative features for phone recognition. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2011.5947494
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