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Context-dependent deterministic plus stochastic model

Authors :
Soheil Khorram
Hossein Sameti
Fahimeh Bahmaninezhad
Source :
2014 12th International Conference on Signal Processing (ICSP).
Publication Year :
2014
Publisher :
IEEE, 2014.

Abstract

This article proposes a method to improve the performance of deterministic plus stochastic model (DSM-) based feature extraction by integrating the contextual information. One precious advantage of speech synthesis over speech recognition is that in both training and testing phases of synthesis, contextual information is available. However, similar to recognition, this invaluable knowledge has been forgotten during acoustic feature extraction of speech synthesis. DSM expresses the residual of Mel-cepstral analysis through a summation of two components, namely deterministic and stochastic. This study proposes to model the deterministic component through a novel context-dependent principal component analysis (CD-PCA), and the stochastic component through the conventional high-pass filtered noise. Furthermore, due to the high dependency of the proposed feature extraction on state boundaries, the feature analysis and HMM-based modeling are performed in an iterative manner. Subjective evaluations conducted on a Persian speech database confirm the effectiveness of the proposed synthesis system.

Details

Database :
OpenAIRE
Journal :
2014 12th International Conference on Signal Processing (ICSP)
Accession number :
edsair.doi...........469b450e15b20a95a66d2885363f6685
Full Text :
https://doi.org/10.1109/icosp.2014.7015067