Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder
By Qiao Tian, Bing Yang, Benlai Tang, Shan Liu. 2018
9 citations recorded by the source corpus.
Although state-of-the-art parallel WaveNet has addressed the issue of real-time waveform generation, there remains problems. Firstly, due to the noisy input signal of the model, there is still a gap between the quality of generated and natural waveforms. Secondly, a parallel WaveNet is trained under a distillation framework, which makes it tedious to adapt a well trained model to a new speaker. To address these two problems, in this paper we propose an end-to-end adaptation method based on the generative adversarial network (GAN), which can reduce the computational cost for the training of new speaker adaptation. Our subjective experiments shows that the proposed training method can further reduce the quality gap between generated and natural waveforms.
Subfields: Artificial Intelligence, Signal Processing
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Citations in the corpus
- Parallel Wavegan: A Fast Waveform Generation Model Based on Generative Adversarial Networks with Multi-Resolution Spectrogram (2019, methodology): “3 Our method adopts least-squares GANs thanks to its training stability [19–22].”
- Probability density distillation with generative adversarial networks for high-quality parallel waveform generation (2019, methodology): “2 This framework adopts a least-squares GANs thanks to its stability during the training process [22, 24, 27, 28].”
- FeatherWave: An efficient high-fidelity neural vocoder with multi-band linear prediction (2020, methodology): “In recent years, the quality of text-to-speech (TTS) has been significantly improved by neural vocoders such as WaveNet [1], Parallel WaveNet [2], WaveRNN [3], LPCNet [4], etc.”