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- W2994653362 abstract "With the emergence of text-to-speech (TTS) systems, the synthetic speech have been widely applied. But the quality evaluation method of the synthetic speech is still away from an agreement. This paper presents a novel method based on residual learning networks, which was trained on the databases of Blizzard Challenge and our own corpus. The effect of the proposed architecture was compared with the LSTM networks and VGG networks. According to the results of experiments, the architecture based on the ResNet gives root mean square error (RMSE) of 0.40 and the correlation coefficient of 0.82, which is better than the other two networks." @default.
- W2994653362 created "2019-12-26" @default.
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- W2994653362 date "2019-12-22" @default.
- W2994653362 modified "2023-09-24" @default.
- W2994653362 title "Naturalness evaluation of synthetic speech based on residual learning networks" @default.
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- W2994653362 doi "https://doi.org/10.1007/978-981-15-2756-2_2" @default.
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