Unsupervised Noise Adaptive Speech Enhancement By Discriminator-constrained Optimal Transport

Abstract

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 for

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keylin2021unsupervised

Related papers

• Neural Domain Alignment For Spoken Language Recognition Based On Optimal Transport (2023)0.00
• Discrete Optimal Transport Is A Strong Audio Adversarial Attack (2025)0.00
• Unsupervised Neural Adaptation Model Based On Optimal Transport For Spoken Language Identification (2020)8.82
• Optimal Transport-based Adaptation In Dysarthric Speech Tasks (2021)0.00
• Channel Adaptation For Speaker Verification Using Optimal Transport With Pseudo Label (2024)0.00
• Interpretable Dysarthric Speaker Adaptation Based On Optimal-transport (2022)2.26
• Unsupervised Adaptation With Domain Separation Networks For Robust Speech Recognition (2017)9.92
• Effective Noise-aware Data Simulation For Domain-adaptive Speech Enhancement Leveraging Dynamic Stochastic Perturbation (2024)3.58