# GAN-Based Speech Enhancement for Low SNR Using Latent Feature Conditioning

arXiv: https://arxiv.org/abs/2410.13599 · HTML: https://mlsanity.com/paper/2410.13599/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2024.

## Built on by (most cited first, 6 of 6)

- [Leveraging Discriminative Latent Representations for Conditioning GAN-Based Speech Enhancement](https://mlsanity.com/paper/2508.20859/) (2025)
- [AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement](https://mlsanity.com/paper/2510.05295/) (2025)
- [Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation](https://mlsanity.com/paper/2505.16911/) (2025)
- [Bridging Self-Supervised Learning and Speech Enhancement: A Wav2Vec2-Conditioned Framework](https://mlsanity.com/paper/2606.22591/) (2026)
- [SlimDiffuSE: Towards Efficient Diffusion-Based Speech Enhancement using Slimmable Networks](https://mlsanity.com/paper/2608.21188/) (2026)
- [Discriminative Flow Matching: Beyond Time-Conditioning in Generative Restoration via Flow-State Representations](https://mlsanity.com/paper/2609.04525/) (2026)

## Abstract (arXiv)

Enhancing speech quality under adverse SNR conditions remains a significant challenge for discriminative deep neural network (DNN)-based approaches. In this work, we propose DisCoGAN, which is a time-frequency-domain generative adversarial network (GAN) conditioned by the latent features of a discriminative model pre-trained for speech enhancement in low SNR scenarios. Our proposed method achieves superior performance compared to state-of-the-arts discriminative methods and also surpasses end-to-end (E2E) trained GAN models. We also investigate the impact of various configurations for conditioning the proposed GAN model with the discriminative model and assess their influence on enhancing speech quality