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- W2798253173 abstract "This paper proposes a deep auto-encoder structure to extract robust spectral features for statistical parametric speech synthesis. The technique allows us to compress the low-dimensional features from high dimensional spectral envelope without degradation for full-band speech in a data-driven way. We carried out a subjective evaluation and found that the optimum auto-encoder architecture. Experimental results showed that an analysis-by-synthesis using the proposed auto-encoder has lower reconstruction error of spectral envelope than conventional mel-cepstral analysis in narrow-band as well as full-band. Our results confirm that the proposed method increases the quality of synthesized speech in text-to-speech experiments." @default.
- W2798253173 created "2018-04-24" @default.
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- W2798253173 date "2018-02-21" @default.
- W2798253173 modified "2023-09-24" @default.
- W2798253173 title "Low-dimensional representation of spectral envelope using deep auto-encoder for speech synthesis" @default.
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- W2798253173 doi "https://doi.org/10.1145/3185066.3185088" @default.
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