The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends
Abstract
Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) are the two most common types of acoustic models used in statistical parametric approaches for generating low-level speech waveforms from high-level symbolic inputs via intermediate acoustic feature sequences. However, these models have their limitations in representing complex, nonlinear relationships between the speech generation inputs and the acoustic features. Inspired by the intrinsically hierarchical process of human speech production and by the successful application of deep neural networks (DNNs) to automatic speech recognition (ASR), deep learning techniques have also been applied successfully to speech generation, as reported in recent literature.
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Cites
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Self-citationTraining Products of Experts by Minimizing Contrastive Divergence
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“( ) v P ; m Because computation of [·] EP Model is intractable, the contrastive divergence (CD) algorithm has been proposed to approximate [·] EP Model by Gibbs sampling [77].”
BackgroundMethodGreedy layer-wise training of deep networks
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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
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Cited by
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
Owens, A., Isola, P., McDermott, J., Torralba, A., Adelson, E. H., & Freeman, W. T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.264 · Cited by 346
(2017) · International Conference on Learning Representations · Cited by 314
Merlin: An Open Source Neural Network Speech Synthesis System
Wu, Z., Watts, O., & King, S. (2016) · 9th ISCA Workshop on Speech Synthesis Workshop (SSW 9) · doi:10.21437/ssw.2016-33 · Cited by 299
A bird's-eye view of deep learning in bioimage analysis
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Metadata
- Authors
- Ling, Z.-H., Kang, S.-Y., Zen, H., Senior, A., Schuster, M., Qian, X.-J., Meng, H. M., & Deng, L.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2015-05
- Volume
- 32
- Issue
- 3
- Pages
- 35-52
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2043003570
- Cited by
- 241
- References
- 6
- FWCI
- 12.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ling, Z.-H., Kang, S.-Y., Zen, H., Senior, A., Schuster, M., Qian, X.-J., Meng, H. M., & Deng, L. (2015). Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2014.2359987
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