The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis
Open accessAbstract
Deep neural networks (DNNs) use a cascade of hidden representations to enable the learning of complex mappings from input to output features. They are able to learn the complex mapping from text-based linguistic features to speech acoustic features, and so perform text-to-speech synthesis. Recent results suggest that DNNs can produce more natural synthetic speech than conventional HMM-based statistical parametric systems. In this paper, we show that the hidden representation used within a DNN can be improved through the use of Multi-Task Learning, and that stacking multiple frames of hidden layer activations (stacked bottleneck features) also leads to improvements. Experimental results confirmed the effectiveness of the proposed methods, and in listening tests we find that stacked bottleneck features in particular offer a significant improvement over both a baseline DNN and a benchmark HMM system.
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A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Caruana, R. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_5 · Cited by 577
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Zhang, Y., & Yang, Q. (2017) · arXiv · doi:10.48550/arxiv.1707.08114 · Cited by 2,910
Deep visual tracking: Review and experimental comparison
Li, P., Wang, D., Wang, L., & Lu, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.11.007 · Cited by 517
A neural network multi-task learning approach to biomedical named entity recognition
Crichton, G., Pyysalo, S., Chiu, B., & Korhonen, A. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1776-8 · Cited by 229
Yao, Z., Wang, Z., Liu, W., Liu, Y., & Pan, J. (2020) · Speech Communication · doi:10.1016/j.specom.2020.03.005 · Cited by 152
On-edge multi-task transfer learning: model and practice with data-driven task allocation
Chen, Q., Zheng, Z., Hu, C., Wang, D., & Liu, F. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2019.2962435 · Cited by 88
Metadata
- Authors
- Wu, Z., Valentini-Botinhao, C., Watts, O., & King, S.
- Venue
- 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2015-04
- Pages
- 4460-4464
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1499332833
- Cited by
- 238
- References
- 3
- FWCI
- 25.00
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wu, Z., Valentini-Botinhao, C., Watts, O., & King, S. (2015). Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2015.7178814
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