WaveNODE: A Continuous Normalizing Flow for Speech Synthesis

By Hyeongju Kim, Hyeongseung Lee, Woohyun Kang, Sung Jun Cheon, Byoung Jin Choi, Nam Soo Kim. 2020

12 citations recorded by the source corpus.

In recent years, various flow-based generative models have been proposed to generate high-fidelity waveforms in real-time. However, these models require either a well-trained teacher network or a number of flow steps making them memory-inefficient. In this paper, we propose a novel generative model called WaveNODE which exploits a continuous normalizing flow for speech synthesis. Unlike the conventional models, WaveNODE places no constraint on the function used for flow operation, thus allowing the usage of more flexible and complex functions. Moreover, WaveNODE can be optimized to maximize the likelihood without requiring any teacher network or auxiliary loss terms. We experimentally show that WaveNODE achieves comparable performance with fewer parameters compared to the conventional flow-based vocoders.

Subfields: Artificial Intelligence, Speech & Audio, Signal Processing

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Citations in the corpus

WaveGrad: Estimating Gradients for Waveform Generation (2020, background): “, 2019), and continuous normalizing flows (Kim et al., 2020; Wu & Ling, 2020), implicit generative models such as generative adversarial networks (GAN) (Donahue et al.”