The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Extracting deep bottleneck features using stacked auto-encoders
Open accessAbstract
In this work, a novel training scheme for generating bottleneck features from deep neural networks is proposed. A stack of denoising auto-encoders is first trained in a layer-wise, unsupervised manner. Afterwards, the bottleneck layer and an additional layer are added and the whole network is fine-tuned to predict target phoneme states. We perform experiments on a Cantonese conversational telephone speech corpus and find that increasing the number of auto-encoders in the network produces more useful features, but requires pre-training, especially when little training data is available. Using more unlabeled data for pre-training only yields additional gains. Evaluations on larger datasets and on different system setups demonstrate the general applicability of our approach. In terms of word error rate, relative improvements of 9.2% (Cantonese, ML training), 9.3% (Tagalog, BMMI-SAT training), 12% (Tagalog, confusion network combinations with MFCCs), and 8.7% (Switchboard) are achieved.
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Cites
A fast learning algorithm for deep belief nets
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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
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
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Phoneme recognition using time-delay neural networks
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Self-citationSWITCHBOARD: telephone speech corpus for research and development
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Deep Neural Networks for Acoustic Modeling in Speech Recognition
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Connectionist Speech Recognition: A Hybrid Approach
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Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
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A survey on deep learning for big data
Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.006 · Cited by 1,226
Mobile sensor data anonymization
Malekzadeh, M., Clegg, R. G., Cavallaro, A., & Haddadi, H. (2019) · Proceedings of the International Conference on Internet of Things Design and Implementation · doi:10.1145/3302505.3310068 · Cited by 298
Caffeine: Toward Uniformed Representation and Acceleration for Deep Convolutional Neural Networks
Zhang, C., Sun, G., Fang, Z., Zhou, P., Pan, P., & Cong, J. (2019) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2017.2785257 · Cited by 270
Metadata
- Authors
- Gehring, J., Miao, Y., Metze, F., & Waibel, A.
- Venue
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 3377-3381
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1970088388
- Cited by
- 274
- References
- 21
- FWCI
- 17.35
- Type
- conference paper
- Language
- en
- Open access
- green
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- Gehring, J., Miao, Y., Metze, F., & Waibel, A. (2013). Extracting deep bottleneck features using stacked auto-encoders. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.1109/icassp.2013.6638284
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