3 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
Robust MelGAN: A robust universal neural vocoder for high-fidelity TTS
Kun Song, Jian Cong, Xinsheng Wang +4
In current two-stage neural text-to-speech (TTS) paradigm, it is ideal to have a universal neural vocoder, once trained, which is robust to imperfect mel-spectrogram predicted from…
Multi-Speaker Expressive Speech Synthesis via Multiple Factors Decoupling
Xinfa Zhu, Yi Lei, Kun Song +3
This paper aims to synthesize the target speaker's speech with desired speaking style and emotion by transferring the style and emotion from reference speech recorded by other spea…
DSPGAN: a GAN-based universal vocoder for high-fidelity TTS by time-frequency domain supervision from DSP
Kun Song, Yongmao Zhang, Yi Lei +5
Recent development of neural vocoders based on the generative adversarial neural network (GAN) has shown obvious advantages of generating raw waveform conditioned on mel-spectrogra…
AdaVITS: Tiny VITS for Low Computing Resource Speaker Adaptation
Kun Song, Heyang Xue, Xinsheng Wang +6
Speaker adaptation in text-to-speech synthesis (TTS) is to finetune a pre-trained TTS model to adapt to new target speakers with limited data. While much effort has been conducted…