# Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition

arXiv: https://arxiv.org/abs/1711.05747 · HTML: https://mlsanity.com/paper/1711.05747/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2017.

## Built on by (most cited first, 19 of 19)

- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022)
- [AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement](https://mlsanity.com/paper/2501.15417/) (2025)
- [SRTNet: Time Domain Speech Enhancement Via Stochastic Refinement](https://mlsanity.com/paper/2210.16805/) (2022)
- [Collaborative Learning to Generate Audio-Video Jointly](https://mlsanity.com/paper/2104.02656/) (2021)
- [SkipConvGAN: Monaural Speech Dereverberation using Generative Adversarial Networks via Complex Time-Frequency Masking](https://mlsanity.com/paper/2211.12623/) (2022)
- [Semi-supervised source localization in reverberant environments with deep generative modeling](https://mlsanity.com/paper/2101.10636/) (2021)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [Blind Restoration of Real-World Audio by 1D Operational GANs](https://mlsanity.com/paper/2212.14618/) (2022)
- [ProSE: Diffusion Priors for Speech Enhancement](https://mlsanity.com/paper/2503.06375/) (2025)
- [Towards Robust Real-time Audio-Visual Speech Enhancement](https://mlsanity.com/paper/2112.09060/) (2021)
- [RNNoise-Ex: Hybrid Speech Enhancement System based on RNN and Spectral Features](https://mlsanity.com/paper/2105.11813/) (2021)
- [Efficient acoustic feature transformation in mismatched environments using a Guided-GAN](https://mlsanity.com/paper/2210.00721/) (2022)
- [Adversarial training of Keyword Spotting to Minimize TTS Data Overfitting](https://mlsanity.com/paper/2408.10463/) (2024)
- [Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching](https://mlsanity.com/paper/2506.16127/) (2025)
- [When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Systems](https://mlsanity.com/paper/2512.17562/) (2025)
- [SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points](https://mlsanity.com/paper/2111.04436/) (2021)
- [Speech Separation based on Contrastive Learning and Deep Modularization](https://mlsanity.com/paper/2305.10652/) (2023)
- [Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits](https://mlsanity.com/paper/2602.15405/) (2026)

## Abstract (arXiv)

We investigate the effectiveness of generative adversarial networks (GANs) for speech enhancement, in the context of improving noise robustness of automatic speech recognition (ASR) systems. Prior work demonstrates that GANs can effectively suppress additive noise in raw waveform speech signals, improving perceptual quality metrics; however this technique was not justified in the context of ASR. In this work, we conduct a detailed study to measure the effectiveness of GANs in enhancing speech contaminated by both additive and reverberant noise. Motivated by recent advances in image processing, we propose operating GANs on log-Mel filterbank spectra instead of waveforms, which requires less computation and is more robust to reverberant noise. While GAN enhancement improves the performance of a clean-trained ASR system on noisy speech, it falls short of the performance achieved by conventional multi-style training (MTR). By appending the GAN-enhanced features to the noisy inputs and retraining, we achieve a 7% WER improvement relative to the MTR system.