  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.
