Work

PhaseAug: A Differentiable Augmentation for Speech Synthesis to Simulate One-to-Many Mapping

Year: 2023

Type: conference-paper

Abstract: Previous generative adversarial network (GAN)-based neural vocoders are trained to reconstruct the exact ground truth wave-form from the paired mel-spectrogram and do not consider the one-to-many relationship of speech synthesis. This conventional training causes overfitting for both the discriminators and the generator, leading to the periodicity artifacts in the generated audio signal. In this work, we present PhaseAug, the first differentiable augmentation for speech synthesis that rotates the phase of each frequency bin to simulate one-to-many mapping. With our proposed method, we outperform baselines without any architecture modification. Code and audio samples will be available at https://github.com/mindslab-ai/phaseaug.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp49357.2023.10096374

Authors: Junhyeok Lee, Seungu Han, Hyunjae Cho, Wonbin Jung

Institutions: Seoul National University, Korea Advanced Institute of Science and Technology

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Korea Advanced Institute of Science and Technology, Iran Telecommunication Research Center, Institute for Information and Communications Technology Promotion

OpenAlex ID: W4375868903

FWCI: 1.322

Cites: 30

Cited by: 7

Related to: 10