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Statistical parametric speech synthesis using deep neural networks

Zen, Heiga, Senior, Andrew, Schuster, Mike·2013·2013 IEEE International Conference on Acoustics, Speech and Signal Processing
DOI 10.1109/icassp.2013.6639215
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Abstract

Conventional approaches to statistical parametric speech synthesis typically use decision tree-clustered context-dependent hidden Markov models (HMMs) to represent probability densities of speech parameters given texts. Speech parameters are generated from the probability densities to maximize their output probabilities, then a speech waveform is reconstructed from the generated parameters. This approach is reasonably effective but has a couple of limitations, e.g. decision trees are inefficient to model complex context dependencies. This paper examines an alternative scheme that is based on a deep neural network (DNN). The relationship between input texts and their acoustic realizations is modeled by a DNN. The use of the DNN can address some limitations of the conventional approach. Experimental results show that the DNN-based systems outperformed the HMM-based systems with similar numbers of parameters.

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Metadata

Authors
Zen, H., Senior, A., & Schuster, M.
Venue
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Publisher
IEEE
Published
2013-05
Pages
7962-7966
ISSN
1520-6149 (ISSN-L)
DOI
10.1109/icassp.2013.6639215
OpenAlex
W2102003408
Cited by
809
References
1
FWCI
39.24
Type
conference paper
Language
en
Open access
closed
Licence

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CoChat. (2026). Citation check: Statistical parametric speech synthesis using deep neural networks. https://app.cochat.ai/papers/10.1109/icassp.2013.6639215
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Zen, H., Senior, A., & Schuster, M. (2013). Statistical parametric speech synthesis using deep neural networks. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.1109/icassp.2013.6639215

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