Unified Source-Filter GAN: Unified Source-filter Network Based On Factorization of Quasi-Periodic Parallel WaveGAN

By Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda. 2021

13 citations recorded by the source corpus.

We propose a unified approach to data-driven source-filter modeling using a single neural network for developing a neural vocoder capable of generating high-quality synthetic speech waveforms while retaining flexibility of the source-filter model to control their voice characteristics. Our proposed network called unified source-filter generative adversarial networks (uSFGAN) is developed by factorizing quasi-periodic parallel WaveGAN (QPPWG), one of the neural vocoders based on a single neural network, into a source excitation generation network and a vocal tract resonance filtering network by additionally implementing a regularization loss. Moreover, inspired by neural source filter (NSF), only a sinusoidal waveform is additionally used as the simplest clue to generate a periodic source excitation waveform while minimizing the effect of approximations in the source filter model. The experimental results demonstrate that uSFGAN outperforms conventional neural vocoders, such as QPPWG and NSF in both speech quality and pitch controllability.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Frank K. Soong
5. Sheng Zhao
6. Yi Ren
7. Zhou Zhao
8. Lei He

In Signal Processing

1. Xu Tan
2. Tie-Yan Liu
3. Tao Qin
4. Frank K. Soong
5. Sheng Zhao
6. Yi Ren
7. Zhou Zhao
8. Lei He

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, background): “Other Vocoders Some works leverage neural-based source-filter model for waveform generation [375, 374, 371, 207, 146, 145, 75, 305, 407], aiming to achieve high voice quality while maintaining controllable speech generation.”