# Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport arXiv: https://arxiv.org/abs/2111.06316 ยท HTML: https://mlsanity.com/paper/2111.06316/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 52 references that are in the map. - First posted on arXiv 11 Nov 2021. ## Builds on (most cited first, 26 of 26) - [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015) - [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016) - [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017) - [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014) - [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017) - [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016) - [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013) - [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014) - [Optimal Transport for Domain Adaptation](https://mlsanity.com/paper/1507.00504/) (2015) - [Joint Distribution Optimal Transportation for Domain Adaptation](https://mlsanity.com/paper/1705.08848/) (2017) - [DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors](https://mlsanity.com/paper/2010.15258/) (2020) - [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017) - [DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1803.10081/) (2018) - [Incremental Learning of Object Detectors without Catastrophic Forgetting](https://mlsanity.com/paper/1708.06977/) (2017) - [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017) - [MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement](https://mlsanity.com/paper/2104.03538/) (2021) - [Theoretical Analysis of Domain Adaptation with Optimal Transport](https://mlsanity.com/paper/1610.04420/) (2016) - [Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation](https://mlsanity.com/paper/2010.05862/) (2020) - [Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1711.10288/) (2017) - [Transfer Learning for Speech and Language Processing](https://mlsanity.com/paper/1511.06066/) (2015) - [Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention](https://mlsanity.com/paper/2002.05873/) (2020) - [Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification](https://mlsanity.com/paper/1709.01703/) (2017) - [Noise Adaptive Speech Enhancement using Domain Adversarial Training](https://mlsanity.com/paper/1807.07501/) (2018) - [Invariant Representations for Noisy Speech Recognition](https://mlsanity.com/paper/1612.01928/) (2016) - [SERIL: Noise Adaptive Speech Enhancement using Regularization-based Incremental Learning](https://mlsanity.com/paper/2005.11760/) (2020) - [Domain-adaptive Fall Detection Using Deep Adversarial Training](https://mlsanity.com/paper/2012.10911/) (2020) ## Built on by (most cited first, 16 of 16) - [Large Language Models are Efficient Learners of Noise-Robust Speech Recognition](https://mlsanity.com/paper/2401.10446/) (2024) - [Pre-training for Speech Translation: CTC Meets Optimal Transport](https://mlsanity.com/paper/2301.11716/) (2023) - [Self-Supervised Visual Acoustic Matching](https://mlsanity.com/paper/2307.15064/) (2023) - [Unsupervised Noise adaptation using Data Simulation](https://mlsanity.com/paper/2302.11981/) (2023) - [Noise-aware Speech Enhancement using Diffusion Probabilistic Model](https://mlsanity.com/paper/2307.08029/) (2023) - [Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition](https://mlsanity.com/paper/2209.12172/) (2022) - [Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10563/) (2023) - [A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and Recognition](https://mlsanity.com/paper/2405.16952/) (2024) - [Temporal Order Preserved Optimal Transport-based Cross-modal Knowledge Transfer Learning for ASR](https://mlsanity.com/paper/2409.02239/) (2024) - [Transfer Learning Analysis of Variational Quantum Circuits](https://mlsanity.com/paper/2501.01507/) (2025) - [Linguistic Knowledge Transfer Learning for Speech Enhancement](https://mlsanity.com/paper/2503.07078/) (2025) - [NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling](https://mlsanity.com/paper/2206.09058/) (2022) - [Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR](https://mlsanity.com/paper/2505.13079/) (2025) - [Partial Coupling of Optimal Transport for Spoken Language Identification](https://mlsanity.com/paper/2203.17036/) (2022) - [Test-time adaptation for speech enhancement with an autoregressive speech prior](https://mlsanity.com/paper/2609.03622/) (2026) - [Optimal transport meets speech: a tutorial review](https://mlsanity.com/paper/2609.31787/) (2026) ## Abstract (arXiv) This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essential regression task in speech processing. The DOTN aims to estimate clean references of noisy speech in a target domain, by exploiting the knowledge available from the source domain. The domain shift between training and testing data has been reported to be an obstacle to learning problems in diverse fields. Although rich literature exists on unsupervised domain adaptation for classification, the methods proposed, especially in regressions, remain scarce and often depend on additional information regarding the input data. The proposed DOTN approach tactically fuses the optimal transport (OT) theory from mathematical analysis with generative adversarial frameworks, to help evaluate continuous labels in the target domain. The experimental results on two SE tasks demonstrate that by extending the classical OT formulation, our proposed DOTN outperforms previous adversarial domain adaptation frameworks in a purely unsupervised manner.