VStyclone: Real-time Chinese voice style clone

Wu, Yicheng; Zhao, Haitao; Liang, Xiaoman; Sun, Yaqi

doi:10.1016/j.compeleceng.2022.108534

Cited by 1 publication

(1 citation statement)

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“…ZSE-VITS (zero-shot expressive variation inference with adversarial learning for end-to-end Text-to-Speech) made use of prosody information prediction and prosody fusion methods to improve the synthesis model performance [25]. The VStyclone model adopted a tone extractor for style extraction [26]. Chen et al proposed a neural fusion architecture, which included a text encoder, an acoustic decoder and a phoneme-level reference encoder.…”

Section: Introductionmentioning

confidence: 99%

Chinese personalised text‐to‐speech synthesis for robot human–machine interaction

Pang,

Teng,

et al. 2023

IET Cyber-Syst and Robotics

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Speech interaction is an important means of robot interaction. With the rapid development of deep learning, end‐to‐end speech synthesis methods based on this technique have gradually become mainstream. Chinese deep learning‐based speech synthesis techniques suffer from problems such as unstable synthesised speech, poor naturalness and poor personalised speech synthesis, which do not satisfy some practical application scenarios. Hence, an F‐MelGAN model is adopted to improve the performance of Chinese speech synthesis. A post‐processing network is used to refine the Mel‐spectrum predicted by the decoder and alleviate the Mel‐spectrum distortion phenomenon. A phoneme‐level and sentence‐level combined module is proposed to model the personalised style of speakers. A combination of an acoustic conditioning network, speaker encoder network GCNet and feedback‐constrained training is proposed to solve the problem of poor personalised speech synthesis and achieve personalised speech customisation in Chinese. Experimental results show that the whole model can generate high‐quality speech with high speaker similarity for both speakers that appear in the training process and speakers that never appear in the training process.

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Section: Introductionmentioning

confidence: 99%