A generative adversarial network (GAN)-based vocoder trained with an adversarial discriminator is commonly used for speech synthesis because of its fast, lightweight, and high-quality characteristics. However, this data-driven model requires a large amount of training data incurring high data-collection costs. This fact motivates us to train a GAN-based vocoder on limited data. A promising solution is to augment the training data to avoid overfitting. However, a standard discriminator is unconditional and insensitive to distributional changes caused by data augmentation. Thus, augmented speech (which can be extraordinary) may be considered real speech. To address this issue, we propose an augmentation-conditional discriminator (AugCondD or ACD for short) that receives the augmentation state as input in addition to speech, thereby assessing input speech according to augmentation state, without inhibiting the learning of the original non-augmented distribution. Experimental results indicate that AugCondD improves speech quality under limited data conditions while achieving comparable speech quality under sufficient data conditions.
II. Results with different network architectures (related to Table 2)
Dataset: LJSpeech
Data
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
HiFiV2-mix
1%
HiFiV2-ACD-mix
1%
iSTFT-mix
1%
iSTFT-ACD-mix
1%
III. Results with different augmentation methods (related to Table 3)
Dataset: LJSpeech
Data
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
HiFi-rate
1%
HiFi-ACD-rate
1%
IV. Results for different speakers (related to Table 4)
Dataset: LibriTTS, ID 260
ID
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Ground truth
260
HiFi-mix
260
HiFi-ACD-mix
260
Dataset: LibriTTS, ID 1580
ID
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Ground truth
1580
HiFi-mix
1580
HiFi-ACD-mix
1580
Citation
@inproceedings{kaneko2024augcondd,
title={Training Generative Adversarial Network-Based Vocoder with Limited Data Using Augmentation-Conditional Discriminator},
author={Kaneko, Takuhiro and Kameoka, Hirokazu and Tanaka, Kou},
booktitle={ICASSP},
year={2024},
}