Training Generative Adversarial Network-Based Vocoder with Limited Data Using Augmentation-Conditional Discriminator

Core Concepts

AugCondD improves speech quality under limited data conditions while maintaining comparable quality under sufficient data conditions.

Abstract

Introduction to GAN-based Vocoder:

Two-stage approach in TTS and VC systems.
Neural vocoder models: autoregressive and non-autoregressive.

Data Augmentation for GAN-based Vocoder:

Strategies like mixup, CutMix, SpecAugment used.
Standard discriminator limitations with data augmentation.

Augmentation-Conditional Discriminator (AugCondD):

Proposal to address standard discriminator limitations.
AugCondD architecture and functionality explained.

Experiments and Results:

Benchmark performance on LJSpeech dataset with AugCondD.
General utility evaluation under various settings.

Conclusion:

AugCondD's effectiveness in training GAN-based vocoders with limited data highlighted.

Stats

"Experimental results indicate that AugCondD improves speech quality under limited data conditions while achieving comparable speech quality under sufficient data conditions."
"The total losses are expressed as follows: LG = LAdv(G) + λFMLFM + λMelLMel."

Quotes

"Augmented speech may be considered real speech due to standard discriminator limitations."
"AugCondD allows assessing input speech considering the augmentation state."

Key Insights Distilled From

Training Generative Adversarial Network-Based Vocoder with Limited Data Using Augmentation-Conditional Discriminator

by Takuhiro Kan... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16464.pdf

Deeper Inquiries

How can the proposed AugCondD be applied to other models beyond GAN-based vocoders

The proposed AugCondD can be applied to other models beyond GAN-based vocoders by adapting the concept of conditioning on augmentation states. For instance, in image processing tasks like style transfer or image generation, incorporating an augmentation-conditional discriminator could help maintain the fidelity and quality of generated images while preventing overfitting due to data augmentation. Similarly, in natural language processing tasks such as text generation or machine translation, AugCondD could assist in ensuring that augmented text samples are appropriately evaluated without bias from extreme augmentations.

What are the potential drawbacks or criticisms of using an augmentation-conditional discriminator like AugCondD

One potential drawback of using an augmentation-conditional discriminator like AugCondD is the added complexity it introduces to the model architecture. The need for additional input (the augmentation state) and modifications to the discriminator may increase computational costs and training time. Moreover, designing effective strategies for defining and utilizing meaningful augmentation states across different datasets and tasks could pose a challenge. Additionally, there might be concerns about interpretability and generalization when relying heavily on conditional information related to data augmentations.

How might the concept of conditioning on augmentation states be relevant in other machine learning domains

The concept of conditioning on augmentation states can be relevant in various machine learning domains beyond speech synthesis vocoders. In computer vision applications such as object detection or image classification, incorporating information about how images have been augmented during training could enhance model robustness against variations in input data. In reinforcement learning scenarios where agents interact with environments subject to perturbations or noise, conditioning policies on specific types of environmental changes through augmentation states could lead to more adaptive and stable learning processes. This approach may also find utility in anomaly detection systems where understanding how data has been transformed before inference can improve anomaly identification accuracy under diverse conditions.