Improving Deep Neural Network Based Speech Synthesis through Contextual Feature Parametrization and Multi-Task Learning

Improving Deep Neural Network Based Speech Synthesis through Contextual Feature Parametrization... We propose three techniques to improve speech synthesis based on deep neural network (DNN). First, at the DNN input we use real-valued contextual feature vector to represent phoneme identity, part of speech and pause information instead of the conventional binary vector. Second, at the DNN output layer, parameters for pitch-scaled spectrum and aperiodicity measures are estimated for constructing the excitation signal used in our baseline synthesis vocoder. Third, the bidirectional recurrent neural network architecture with long short term memory (BLSTM) units is adopted and trained with multi-task learning for DNN-based speech synthesis. Experimental results demonstrate that the quality of synthesized speech has been improved by adopting the new input vector and output parameters. The proposed BLSTM architecture for DNN is also beneficial to learning the mapping function from the input contextual feature to the speech parameters and to improve speech quality. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Signal Processing Systems Springer Journals

Improving Deep Neural Network Based Speech Synthesis through Contextual Feature Parametrization and Multi-Task Learning

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Engineering; Signal,Image and Speech Processing; Circuits and Systems; Electrical Engineering; Image Processing and Computer Vision; Pattern Recognition; Computer Imaging, Vision, Pattern Recognition and Graphics
ISSN
1939-8018
eISSN
1939-8115
D.O.I.
10.1007/s11265-017-1293-z
Publisher site
See Article on Publisher Site

Abstract

We propose three techniques to improve speech synthesis based on deep neural network (DNN). First, at the DNN input we use real-valued contextual feature vector to represent phoneme identity, part of speech and pause information instead of the conventional binary vector. Second, at the DNN output layer, parameters for pitch-scaled spectrum and aperiodicity measures are estimated for constructing the excitation signal used in our baseline synthesis vocoder. Third, the bidirectional recurrent neural network architecture with long short term memory (BLSTM) units is adopted and trained with multi-task learning for DNN-based speech synthesis. Experimental results demonstrate that the quality of synthesized speech has been improved by adopting the new input vector and output parameters. The proposed BLSTM architecture for DNN is also beneficial to learning the mapping function from the input contextual feature to the speech parameters and to improve speech quality.

Journal

Journal of Signal Processing SystemsSpringer Journals

Published: Oct 2, 2017

References

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