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- W2142918400 abstract "Currently, the statistical framework based on Hidden Markov Models (HMMs) plays a relevant role in speech synthesis, while voice conversion systems based on Gaussian Mixture Models (GMMs) are almost standard. In both cases, statistical modeling is applied to learn distributions of acoustic vectors extracted from speech signals, each vector containing a suitable parametric representation of one speech frame. The overall performance of the systems is often limited by the accuracy of the underlying speech parameterization and reconstruction method. The method presented in this paper allows accurate MFCC extraction and high-quality reconstruction of speech signals assuming a Harmonics plus Noise Model (HNM). Its suitability for high-quality HMM-based speech synthesis is shown through subjective tests." @default.
- W2142918400 created "2016-06-24" @default.
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- W2142918400 date "2011-05-01" @default.
- W2142918400 modified "2023-10-02" @default.
- W2142918400 title "HNM-based MFCC+F0 extractor applied to statistical speech synthesis" @default.
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- W2142918400 doi "https://doi.org/10.1109/icassp.2011.5947411" @default.
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