A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis

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Authors: Xin Wang,Jaime Lorenzo-Trueba,Shinji Takaki,Lauri Juvela,Junichi Yamagishi
ArXiv: 1804.02549
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Abstract URL: http://arxiv.org/abs/1804.02549v1


Recent advances in speech synthesis suggest that limitations such as the lossy nature of the amplitude spectrum with minimum phase approximation and the over-smoothing effect in acoustic modeling can be overcome by using advanced machine learning approaches. In this paper, we build a framework in which we can fairly compare new vocoding and acoustic modeling techniques with conventional approaches by means of a large scale crowdsourced evaluation. Results on acoustic models showed that generative adversarial networks and an autoregressive (AR) model performed better than a normal recurrent network and the AR model performed best. Evaluation on vocoders by using the same AR acoustic model demonstrated that a Wavenet vocoder outperformed classical source-filter-based vocoders. Particularly, generated speech waveforms from the combination of AR acoustic model and Wavenet vocoder achieved a similar score of speech quality to vocoded speech.

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